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Climate Change: 2013 IPCC Update

4. How do we study the climate system?

  • 4.1 Models, observations and validation
  • 4.2 Why have global temperature increases slowed down in the past 15 years?
  • 4.3 How do projections from previous assessments compare with the present observations?
  • 4.4 How have the oceans changed?
  • 4.5 Snow and Ice
  • 4.6 What are the main climate changes that could be irreversible?
    • 4.6.1 Irreversible changes in ocean circulation
    • 4.6.2 Irreversible changes in release of the permafrost
    • 4.6.3 Irreversible change in Collapse of ice sheets

4.1 Models, observations and validation

The source document for this Digest states:

TS.4 Understanding the Climate System and its Recent Changes

TS.4.1 Introduction

Understanding of the climate system results from combining observations, theoretical studies of feedback processes, and model simulations. Compared to AR4, more detailed observations and improved climate models (see Box TS.4) now enable the attribution of detected changes to human influences in more climate system components. The consistency of observed and modeled changes across the climate system, including regional temperatures, the water cycle, global energy budget, cryosphere and oceans (including ocean acidification), point to global climate change resulting primarily from anthropogenic increases in greenhouse gas concentrations. {10}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TS.4.1 Introduction, p.60

Box TS.4: Model Evaluation

Climate models have continued to be improved since the AR4, and many models have been extended into Earth System Models by including the representation of biogeochemical cycles important to climate change. Box TS.4, Figure 1 provides a partial overview of model capabilities as assessed in this report, including improvements or lack thereof relative to models that were assessed in the AR4 or that were available at the time of the AR4. {9.1, 9.8.1; Box 9.1} The ability of climate models to simulate surface temperature has improved in many, though not all, important aspects relative to the generation of models assessed in the AR4. There continues to be very high confidence that models reproduce the observed large-scale time-mean surface temperature patterns (pattern correlation of ~0.99), although systematic errors of several degrees Celsius are found in some regions. There is high confidence that on the regional scale (sub-continental and smaller), time-mean surface temperature is better simulated than at the time of the AR4; however, confidence in model capability is lower than for the large scale. Models are able to reproduce the magnitude of the observed global-mean or northern- hemisphere-mean temperature variability on interannual to centennial time scales. Models are also able to reproduce the large-scale patterns of temperature during the Last Glacial Maximum (Section 9.4.1.5) indicating an ability to simulate a climate state much different from the present (see also Box TS.5). {9.4.1, 9.6.1}

There is very high confidence that models reproduce the general features of the global and annual mean surface temperature changes over the historical period, including the warming in the second half of the 20th century and the cooling immediately following large volcanic eruptions. Most simulations of the historical period do not reproduce the observed reduction in global-mean surface warming trend over the last 10–15 years (see Box TS.3). There is medium confidence that the trend difference between models and observations during 1998–2012 is to a substantial degree caused by internal variability, with possible contributions from forcing inadequacies in models and some models overestimating the response to increasing greenhouse-gas forcing. Most, though not all, models overestimate the observed warming trend in the tropical troposphere over the last 30 years, and tend to underestimate the long-term lower-stratospheric cooling trend. {9.4.1; Box 9.2}

The simulation of large-scale patterns of precipitation has improved somewhat since the AR4, although models continue to perform less well for precipitation than for surface temperature. The spatial pattern correlation between modelled and observed annual mean precipitation has increased from 0.77 for models available at the time of the AR4 to 0.82 for current models. At regional scales, precipitation is not simulated as well, and the assessment remains difficult owing to observational uncertainties. {9.4.1, 9.6.1}

Many models are able to reproduce the observed changes in upper-ocean heat content from 1960 to present. The time series of the multi-model mean falls within the range of the available observational estimates for most of the period. {9.4.2} There is robust evidence that the downward trend in Arctic summer sea-ice extent is better simulated than at the time of the AR4. About one-quarter of the models show a trend as strong as, or stronger, than the trend in observations over the satellite era 1979–2012. Most models simulate a small decreasing trend in Antarctic sea-ice extent, albeit with large inter-model spread, in contrast to the small increasing trend in observations. {9.4.3)

There has been substantial progress since the AR4 in the assessment of model simulations of extreme events. Changes in the frequency of extreme warm and cold days and nights over the second half of the 20th century are consistent between models and observations, with the ensemble-mean global-mean time series generally falling within the range of observational estimates. The majority of models underestimate the sensitivity of extreme precipitation to temperature variability or trends, especially in the tropics. {9.5.4}

In about two-thirds of the models that include an interactive carbon cycle, the simulated global land and ocean carbon sinks over the latter part of the 20th century fall within the range of observational estimates. However, models systematically underestimate the northern-hemisphere land sink implied by atmospheric inversion techniques. {9.4.5}

Regional downscaling methods provide climate information at the smaller scales needed for many climate impact studies. There is high confidence that downscaling adds value both in regions with highly variable topography and for various small-scale phenomena. {9.6.4} The model spread in equilibrium climate sensitivity ranges from 2.1°C to 4.7°C and is very similar to the assessment in the AR4. There is very high confidence that the primary factor contributing to the spread in equilibrium climate sensitivity continues to be the cloud feedback. This applies to both the modern climate and the last glacial maximum. There is likewise very high confidence that, consistent with observations, models show a strong positive correlation between tropospheric temperature and water vapour on regional to global scales, implying a positive water-vapour feedback in both models and observations. {5.3.3, 9.4.1, 9.7}

Climate models are based on physical principles, and they reproduce many important elements of observed climate. Both aspects contribute to our confidence in the models’ suitability for their application in detection and attribution studies (see Chapter 10) and for quantitative future predictions and projections (see Chapters 11–14). There is increasing evidence that some elements of observed variability or trends are well correlated with inter-model differences in model projections for quantities such as Arctic summer sea-ice trends, the snow–albedo feedback, and the carbon loss from tropical land. However, there is still no universal strategy for transferring a model’s past performance to a relative weight of this model in a multi-model-ensemble mean of climate projections. {9.8.3}

The figure highlights the following key features, with the sections that back up the assessment added in brackets: (a) Trends in: AntSIE: Antarctic sea-ice extent {9.4.3} ArctSIE: Arctic sea-ice extent {9.4.3} fgCO2 : Global ocean carbon sink {9.4.5} LST: Lower-stratospheric temperature {9.4.1.} NBP: Global land carbon sink {9.4.5} OHC: Global ocean heat content {9.4.2} TotalO3: Total-column ozone {9.4.1} TAS: Surface air temperature {9.4.1} TTT: Tropical tropospheric temperature {9.4.1} (b) Extremes: Droughts: Droughts {9.5.4} Hurric-hr : Year-to-year count of Atlantic hurricanes in high-resolution AGCMs {9.5.4} PR_ext: Global distribution of precipitation extremes {9.5.4} PR_ext-hr: Global distribution of precipitation extremes in high-resolution AGCMs {9.5.4} PR_ext-t: Global trends in precipitation extremes {9.5.4} TAS_ext: Global distributions of surface air temperature extremes {9.5.4} TAS_ext-t: Global trends in surface air temperature extremes {9.5.4} TC: Tropical cyclone tracks and intensity {9.5.4} TC-hr: Tropical cyclone tracks and intensity in high-resolution AGCMs {9.5.4}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , Box TS.4 Model evaluation, p.75-76

Box TS.6: The New RCP Scenarios and CMIP5 Models

Future anthropogenic emissions of greenhouse gases (GHG), aerosol particles and other forcing agents such as land use change are dependent on socio-economic factors, and may be affected by global geopolitical agreements to control those emissions to achieve mitigation. AR4 made extensive use of the SRES scenarios that do not include additional climate initiatives, which means that no scenarios were included that explicitly assume implementation of the United Nations Framework Convention on Climate Change (UNFCCC) or the emissions targets of the Kyoto Protocol. However, GHG emissions are directly affected by non-climate change policies designed for a wide range of other purposes. The SRES scenarios were developed using a sequential approach, i.e., socio-economic factors fed into emissions scenarios, which were then used in simple climate models to determine concentrations of greenhouse gases, and other agents required to drive the more complex atmosphere-ocean global climate models (AOGCMs). In this report, outcomes of climate simulations that use new scenarios (some of which include implied policy actions to achieve mitigation) referred to as “Representative Concentration Pathways” (RCPs) are assessed. These RCPs represent a larger set of mitigation scenarios and were selected to have different targets in terms of radiative forcing at 2100 (about 2.6, 4.5, 6.0 and 8.5 W m–2; see Figure TS.15). The scenarios should be considered plausible and illustrative, and do not have probabilities attached to them. {12.3.1; Box 1.1}

The RCPs were developed using Integrated Assessment Models (IAMs) that typically include economic, demographic, energy, and simple climate components. The emission scenarios they produce are then run through a simple model to produce time series of greenhouse gas concentrations that can be run in AOGCMs. The emission time series from the RCPs can then be used directly in Earth System Models (ESMs) that include interactive biogeochemistry (at least a land and ocean carbon cycle).

The CMIP5 multi-model experiment (coordinated through the World Climate Research Programme) presents an unprecedented level of information on which to base assessments of climate variability and change. CMIP5 includes new ESMs in addition to AOGCMs, new model experiments, and more diagnostic output. CMIP5 is much more comprehensive than the preceding CMIP3 multi-model experiment that was available at the time of the IPCC AR4. CMIP5 has more than twice as many models, many more experiments (that also include experiments to address understanding of the responses in the future climate change scenario runs), and nearly 2  1015 bytes of data (as compared to over 30  1012 bytes of data in CMIP3). A larger number of forcing agents are treated more completely in the CMIP5 models, with respect to aerosols and land use particularly. Black carbon aerosol is now a commonly included forcing agent. Considering CO2 , both ‘concentrations-driven’ projections and ‘emissions-driven’ projections are assessed from CMIP5. These allow quantification of the physical response uncertainties as well as climate-carbon cycle interactions. {1.5.2} The assessment of the mean values and ranges of global mean temperature changes in AR4 would not have been substantially different if the CMIP5 models had been used in that report. The differences in global temperature projections can largely be attributed to the different scenarios. The global mean temperature response simulated by CMIP3 and CMIP5 models is very similar, both in the mean and the model range, transiently and in equilibrium. The range of temperature change across all scenarios is wider because the RCPs include a strong mitigation scenario (RCP2.6) that had no equivalent among the SRES scenarios used in CMIP3. For each scenario, the 5–95% range of the CMIP5 projections is obtained by approximating the CMIP5 distributions by a normal distribution with same mean and standard deviation and assessed as being “likely” for projections of global temperature change for the end of the 21st century. Probabilistic projections with simpler models calibrated to span the range of equilibrium climate sensitivity assessed by the AR4 provide uncertainty ranges that are consistent with those from CMIP5. In AR4 the uncertainties in global temperature projections were found to be approximately constant when expressed as a fraction of the model mean warming (constant fractional uncertainty). For the higher RCPs, the uncertainty is now estimated to be smaller than with the AR4 method for long-term climate change, because the carbon cycle climate feedbacks are not relevant for the concentration driven RCP projections (in contrast, the assessed projection uncertainties of global temperature in AR4 did account of carbon cycle climate feedbacks, even though these were not part of the CMIP3 models). When forced with RCP8.5, CO2 emissions, as opposed to the RCP8.5 CO2 concentrations, CMIP5 Earth System Models (ESMs) with interactive carbon cycle simulate, on average, a 50 (–140 to +210) ppm (CMIP5 model spread) larger atmospheric CO2 concentration and 0.2°C larger global surface temperature increase by 2100. For the low RCPs the fractional uncertainty is larger because internal variability and non-CO2 forcings make a larger relative contribution to the total uncertainty. {12.4.1, 12.4.9} There is overall consistency between the projections of temperature and precipitation based on CMIP3 and CMIP5, both for large-scale patterns and magnitudes of change (Box TS.6, Figure 1). Model agreement and confidence in projections depends on the variable and on spatial and temporal averaging, with better agreement for larger scales. Confidence is higher for temperature than for those quantities related to the water cycle or atmospheric circulation. Improved methods to quantify and display model robustness have been developed to indicate where lack of agreement across models on local trends is a result of internal variability, rather than models actually disagreeing on their forced response. Understanding of the sources and means of characterizing uncertainties in long-term large scale projections of climate change has not changed significantly since AR4, but new experiments and studies have continued to work towards a more complete and rigorous characterization. {9.7.3, 12.2, 12.4.1, 12.4.4, 12.4.5, 12.4.9; Box 12.1}

The well-established stability of geographical patterns of temperature and precipitation change during a transient experiment remains valid in the CMIP5 models (see Box TS.6, Figure 1). Patterns are similar over time and across scenarios and to first order can be scaled by the global mean temperature change. There remain limitations to the validity of this technique when it is applied to strong mitigation scenarios, to scenarios where localized forcing (e.g., aerosols) are significant and vary in time and for variables other than average seasonal mean temperature and precipitation {12.4.2}.

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , Box TS.6 CMIP5 models, p.79-81

4.2 Why have global temperature increases slowed down in the past 15 years?

The source document for this Digest states:

TS.4.2 Surface Temperature

Several advances since the AR4 have allowed a more robust quantification of human influence on surface temperature changes. Observational uncertainty has been explored much more thoroughly than previously and the assessment now considers observations from the first decade of the 21st century and simulations from a new generation of climate models whose ability to simulate historical climate has improved in many respects relative to the previous generation of models considered in AR4. Observed global mean surface temperature anomalies relative to 1880-1919 in recent years lie well outside the range of temperature anomalies in CMIP5 simulations with natural forcing only, but are consistent with the ensemble of CMIP5 simulations including both anthropogenic and natural forcing (Figure TS.9) even though some individual models overestimate the warming trend, while others underestimate it. Simulations with greenhouse gas changes only, and no aerosol changes, generally exhibit stronger warming than has been observed (Figure TS.9). Observed temperature trends over the period 1951–2010, which are characterized by warming over most of the globe with the most intense warming over the Northern Hemisphere continents, are, at most observed locations, consistent with the temperature trends in CMIP5 simulations including anthropogenic and natural forcings and inconsistent with the temperature trends in CMIP5 simulations including natural forcings only. A number of studies have investigated the effects of the Atlantic Multidecadal Oscillation (AMO) on global mean surface temperature. While some studies find a significant role for the AMO in driving multi-decadal variability in GMST, the AMO exhibited little trend over the period 1951-2010 on which these assessments are based, and the AMO is assessed with high confidence to have made little contribution to the GMST trend between 1951 and 2010 (considerably less than 0.1°C). {2.4, 9.8.1, 10.3; FAQ 9.1}.

It is extremely likely that human activities caused more than half of the observed increase in global average surface temperature from 1951 to 2010. This assessment is supported by robust evidence from multiple studies using different methods. In particular, the temperature trend attributable to all anthropogenic forcings combined can be more closely constrained in multi-signal detection and attribution analyses. Uncertainties in forcings and in climate models’ responses to those forcings, together with difficulty in distinguishing the patterns of temperature response due to greenhouse gases and other anthropogenic forcings prevent as precise a quantification of the temperature changes attributable to greenhouse gases and other anthropogenic forcings individually. Consistent with AR4, it is assessed that more than half of the observed increase in global average surface temperature from 1951 to 2010 is very likely due to the observed anthropogenic increase in greenhouse gas concentrations. Greenhouse gases contributed a global mean surface warming likely to be between 0.5°C and 1.3°C over the period between 1951 and 2010, with the contributions from other anthropogenic forcings likely to be between –0.6°C and 0.1°C and from natural forcings likely to be between –0.1°C and 0.1°C. Together these assessed contributions are consistent with the observed warming of approximately 0.6°C over this period (Figure TS.10). {10.3}

Solar forcing is the only known natural forcing acting to warm the climate over the 1951–2010 period but it has increased much less than greenhouse gas forcing, and the observed pattern of long-term tropospheric warming and stratospheric cooling is not consistent with the expected response to solar irradiance variations. Considering this evidence together with the assessed contribution of natural forcings to observed trends over this period, it is assessed that the contribution from solar forcing to the observed global warming since 1951 is extremely unlikely to be larger than that from greenhouse gases. Since solar forcing has very likely decreased since the advent of direct satellite measurements of total solar irradiance in 1978, there is high confidence that changes in total solar irradiance have not contributed to global warming during that period. However, there is medium confidence that the 11-year cycle of solar variability influences decadal climate fluctuations in some regions through amplifying mechanisms. {8.4, 10.3; Box 10.2}

Observed warming over the past sixty years is far outside the range of internal climate variability estimated from pre-instrumental data, and it is also far outside the range of internal variability simulated in climate models. Model-based simulations of internal variability are assessed to be adequate to make this assessment. Further, the spatial pattern of observed warming differs from those associated with internal variability. Based on this evidence, the contribution of internal variability to the 1951–2010 global mean surface temperature trend was assessed to be likely between –0.1°C and 0.1°C, and it is virtually certain that warming since 1951 cannot be explained by internal variability alone. {9.5, 10.3, 10.7}

The instrumental record shows a pronounced warming during the first half of the 20th century. Consistent with AR4, it is assessed that the early 20th century warming is very unlikely to be due to internal variability alone. It remains difficult to quantify the contributions to this early century warming from internal variability, natural forcing and anthropogenic forcing, due to forcing and response uncertainties and incomplete observational coverage. {10.3}

TS.4.3 Atmospheric Temperature

A number of studies since the AR4 have investigated the consistency of simulated and observed trends in free tropospheric temperatures (see section TS.2). Most, though not all, CMIP3 and CMIP5 models overestimate the observed warming trend in the tropical troposphere during the satellite period 1979–2012. Roughly one-half to two-thirds of this difference from the observed trend is due to an overestimate of the sea surface temperature trend, which is propagated upward because models attempt to maintain static stability. There is low confidence in these assessments, however, due to the low confidence in observed tropical tropospheric trend rates and vertical structure. Outside the tropics, and over the period of the radiosonde record beginning in 1961, the discrepancy between simulated and observed trends is smaller. {2.4.4, 9.4, 10.3} Analysis of both radiosonde and satellite datasets, combined with CMIP5 and CMIP3 simulations continues to find that observed tropospheric warming is inconsistent with internal variability and simulations of the response to natural forcings alone. Over the period 1961–2010 CMIP5 models simulate tropospheric warming driven by greenhouse gas changes, with only a small offsetting cooling due to the combined effects of changes in reflecting and absorbing aerosols and tropospheric ozone. Taking this evidence together with the results of multi-signal detection and attribution analyses, it is likely that anthropogenic forcings, dominated by greenhouse gases, have contributed to the warming of the troposphere since 1961. Uncertainties in radiosonde and satellite records makes assessment of causes of observed trends in the upper troposphere less confident than an assessment of the overall atmospheric temperature changes. {2.4.4, 9.4, 10.3}

CMIP5 simulations including greenhouse gas, ozone and natural forcing changes broadly reproduce the observed evolution of lower stratospheric temperature, with some tendency to underestimate the observed cooling trend over the satellite era (see Section TS.2). New studies of stratospheric temperature, considering the responses to natural forcings, greenhouse gases and ozone-depleting substances, demonstrate that it is very likely that anthropogenic forcings, dominated by the depletion of the ozone layer due to ozone depleting substances have contributed to the cooling of the lower stratosphere since 1979. CMIP5 models simulate only a very weak cooling of the lower stratosphere in response to historical greenhouse gas changes, and the influence of greenhouse gases on lower stratospheric temperature has not been formally detected. Considering both regions together, it is very likely that anthropogenic forcing, particularly greenhouse gases and stratospheric ozone depletion, has led to a detectable observed pattern of tropospheric warming and lower stratospheric cooling since 1961. {2.4, 9.4, 10.3}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TS.4.2-TS4.3, p.60-68

Box TS.3: Climate Models and the Hiatus in Global-Mean Surface Warming of the Past 15 years

The observed global-mean surface temperature (GMST) has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years (Box TS.3, Figure 1a,c). Depending on the observational data set, the GMST trend over 1998–2012 is estimated to be around one-third to one-half of the trend over 1951–2012. For example, in HadCRUT4 the trend is 0.04°C per decade over 1998–2012, compared to 0.11°C per decade over 1951–2012. The reduction in observed GMST trend is most marked in Northern-Hemisphere winter. Even with this “hiatus” in GMST trend, the decade of the 2000s has been the warmest in the instrumental record of GMST. Nevertheless, the occurrence of the hiatus in GMST trend during the past 15 years raises the two related questions of what has caused it and whether climate models are able to reproduce it. {2.4.3, 9.4.1; Box 9.2; Table 2.7}

Fifteen-year-long hiatus periods are common in both the observed and CMIP5 historical GMST time series. However, an analysis of the full suite of CMIP5 historical simulations (augmented for the period 2006–2012 by RCP4.5 simulations) reveals that 111 out of 114 realisations show a GMST trend over 1998–2012 that is higher than the entire HadCRUT4 trend ensemble (Box TS.3, Figure 1a; CMIP5 ensemble-mean trend is 0.21°C per decade). This difference between simulated and observed trends could be caused by some combination of (a) internal climate variability, (b) missing or incorrect radiative forcing, and (c) model response error. These potential sources of the difference, which are not mutually exclusive, are assessed below, as is the cause of the observed GMST trend hiatus. {2.4.3, 9.3.2, 9.4.1; Box 9.2} (a) Internal Climate Variability

Hiatus periods of 10–15 years can arise as a manifestation of internal decadal climate variability, which sometimes enhances and sometimes counteracts the long-term externally forced trend. Internal variability thus diminishes the relevance of trends over periods as short as 10 to 15 years for long-term climate change. Furthermore, the timing of internal decadal climate variability is not expected to be matched by the CMIP5 historical simulations, owing to the predictability horizon of at most 10 to 20 years (CMIP5 historical simulations are typically started around nominally 1850 from a control run). However, climate models exhibit individual decades of GMST trend hiatus even during a prolonged phase of energy uptake of the climate system, in which case the energy budget would be balanced by increasing subsurface-ocean heat uptake. {2.4.3, 9.3.2, 11.2.2; Box 2.2, 9.2}

Owing to sampling limitations, it is uncertain whether an increase in the rate of subsurface-ocean heat uptake occurred during the past 15 years. However, it is very likely that the climate system, including the ocean below 700 m depth, has continued to accumulate energy over the period 1998–2010. Consistent with this energy accumulation, global-mean sea level has continued to rise during 1998–2012, at a rate only slightly and insignificantly lower than during 1993–2012. The consistency between observed heat-content and sea- level changes yields high confidence in the assessment of continued ocean energy accumulation, which is in turn consistent with the positive radiative imbalance of the climate system. By contrast, there is limited evidence that the hiatus in GMST trend has been accompanied by a slower rate of increase in ocean heat content over the depth range 0 to 700 m, when comparing the period 2003–2010 against 1971–2010. There is low agreement on this slowdown, since three of five analyses show a slowdown in the rate of increase while the other two show the increase continuing unabated. {3.2.3, 3.2.4, 3.7, 8.5.1, 13.3; Box 3.1, 13.1}

During the 15-year period beginning in 1998, the ensemble of HadCRUT4 GMST trends lies below almost all model-simulated trends (Box TS.3, Figure 1a), whereas during the 15-year period ending in 1998, it lies above 93 out of 114 modelled trends (Box TS.3, Figure 1b; HadCRUT4 ensemble-mean trend 0.26°C per decade, CMIP5 ensemble-mean trend 0.16°C per decade). Over the 62-year period 1951–2012, observed and CMIP5 ensemble-mean trend agree to within 0.02°C per decade (Box TS.3, Figure 1c; CMIP5 ensemble- mean trend 0.13°C per decade). There is hence very high confidence that the CMIP5 models show long-term GMST trends consistent with observations, despite the disagreement over the most recent 15-year period. Due to internal climate variability, in any given 15-year period the observed GMST trend sometimes lies near one end of a model ensemble, an effect that is pronounced in Box TS.3, Figure 1a,b since GMST was influenced by a very strong El Niño event in 1998. Unlike the CMIP5 historical simulations referred to above, some CMIP5 predictions were initialised from the observed climate state during the late 1990s and the early 21st century. There is medium evidence that these initialised predictions show a GMST lower by about 0.05°C to 0.1°C compared to the historical (uninitialised) simulations and maintain this lower GMST during the first few years of the simulation. In some initialised models this lower GMST occurs in part because they correctly simulate a shift, around 2000, from a positive to a negative phase of the Interdecadal Pacific Oscillation. However, the improvement of this phasing of the Interdecadal Pacific Oscillation through initialisation is not universal across the CMIP5 predictions. Moreover, while part of the GMST reduction through initialisation indeed results from initialising at the correct phase of internal variability, another part may result from correcting a model bias that was caused by incorrect past forcing or incorrect model response to past forcing, especially in the ocean. The relative magnitudes of these effects are at present unknown; moreover, the quality of a forecasting system cannot be evaluated from a single prediction (here, a ten-year prediction within the period 1998–2012). Overall, there is medium confidence that initialisation leads to simulations of GMST during 1998– 2012 that are more consistent with the observed trend hiatus than are the uninitialised CMIP5 historical simulations, and that the hiatus is in part a consequence of internal variability that is predictable on the multiyear timescale. {11.1, 11.2.3; Box 11.1, 11.2, 2.5, 9.2;}

(b) Radiative Forcing On decadal to interdecadal timescales and under continually increasing effective radiative forcing (ERF), the forced component of the GMST trend responds to the ERF trend relatively rapidly and almost linearly (medium confidence). The expected forced-response GMST trend is related to the ERF trend by a factor that has been estimated for the 1% per year CO2 increases in the CMIP5 ensemble as 2.0 [1.3 to 2.7] W m–2 oC–1 (90% uncertainty range). Hence, an ERF trend can be approximately converted to a forced-response GMST trend, permitting an assessment of how much of the change in the GMST trends shown in Box TS.3, Figure 1 is due to a change in ERF trend. {Box 9.2}

The AR5 best-estimate ERF trend over 1998–2011 is 0.23 [0.12 to 0.34] W m–2 per decade (90% uncertainty range), which is substantially lower than the trend over 1984–1998 (0.34 [0.24 to 0.44] W m–2 per decade; note that there was a strong volcanic eruption in 1982) and the trend over 1951–2011 (0.30 [0.20 to 0.40] W m–2 per decade; Box TS.3, Figure 1d–f; the end year 2011 is chosen because data availability is more limited than for GMST). The resulting forced-response GMST trend would approximately be 0.13 [0.06 to 0.31] oC per decade, 0.19 [0.10 to 0.40] oC per decade, and 0.17 [0.08 to 0.36] oC per decade for the periods 1998– 2011, 1984–1998, and 1951–2011, respectively (the uncertainty ranges assume that the range of the conversion factor to GMST trend and the range of ERF trend itself are independent). The AR5 best-estimate ERF forcing trend difference between 1998–2011 and 1951–2011 thus might explain about one-half (0.04 oC per decade) of the observed GMST trend difference between these periods (0.06 to 0.08 oC per decade, depending on observational data set). {8.5.2} The reduction in AR5 best-estimate ERF trend over 1998–2011 compared to both 1984–1998 and 1951– 2011 is mostly due to decreasing trends in the natural forcings, -0.14 [-0.24 to -0.04] W m–2 per decade over 1998–2011 compared to 0.0 [-0.01 to +0.01] W m–2 per decade over 1951–2011. Solar forcing went from a relative maximum in 2000 to a relative minimum in 2009, with a peak-to-peak difference of around 0.15 W m–2 and a linear trend over 1998–2011 of around -0.10 W m–2 per decade. Furthermore, a series of small volcanic eruptions has increased the observed stratospheric aerosol loading after 2000, leading to an additional negative ERF linear-trend contribution of around -0.04 W m–2 per decade over 1998–2011 (Box TS.3, Figure 1d,f). By contrast, satellite-derived estimates of tropospheric aerosol optical depth suggests little overall trend in global-mean aerosol optical depth over the last 10 years, implying little change in ERF due to aerosol-radiative interaction (low confidence because of low confidence in aerosol optical depth trend itself). Moreover, because there is only low confidence in estimates of ERF due to aerosol-cloud interaction, there is likewise low confidence in its trend over the last 15 years. {2.2.3, 8.4.2, 8.5.1, 8.5.2, 10.3.1; Box 10.2; Table 8.6, 8.7}

For the periods 1984–1998 and 1951–2011, the CMIP5 ensemble-mean ERF trend deviates from the AR5 best-estimate ERF trend by only 0.01 W m–2 per decade (Box TS.3, Figure 1e, f). After 1998, however, some contributions to a decreasing ERF trend are missing in the CMIP5 models, such as the increasing stratospheric aerosol loading after 2000 and the unusually low solar minimum in 2009. Nonetheless, over 1998–2011 the CMIP5 ensemble-mean ERF trend is lower than the AR5 best-estimate ERF trend by 0.05 W m–2 per decade (Box TS.3, Figure 1d). Furthermore, global-mean aerosol optical depth in the CMIP5 models shows little trend over 1998–2012, similar to the observations. Although the forcing uncertainties are substantial, there are no apparent incorrect or missing global-mean forcings in the CMIP5 models over the last 15 years that could explain the model–observations difference during the warming hiatus. {9.4.6}

(c) Model Response Error The discrepancy between simulated and observed GMST trends during 1998–2012 could be explained in part by a tendency for some CMIP5 models to simulate stronger warming in response to increases in greenhouse-gas concentration than is consistent with observations. Averaged over the ensembles of models assessed in Section 10.3.1.1.3, the best-estimate greenhouse-gas (GHG) and other anthropogenic (OA) scaling factors are less than one (though not significantly so, Figure 10.4), indicating that the model-mean GHG and OA responses should be scaled down to best match observations. This finding provides evidence that some CMIP5 models show a larger response to greenhouse gases and other anthropogenic factors (dominated by the effects of aerosols) than the real world (medium confidence). As a consequence, it is argued in Chapter 11 that near-term model projections of GMST increase should be scaled down by about 10%. This downward scaling is, however, not sufficient to explain the model-mean overestimate of GMST trend over the hiatus period. {10.3.1; 11.3.6}

Another possible source of model error is the poor representation of water vapour in the upper atmosphere. It has been suggested that a reduction in stratospheric water vapour after 2000 caused a reduction in downward longwave radiation and hence a surface-cooling contribution, possibly missed by the models. However, this effect is assessed here to be small, because there was a recovery in stratospheric water vapour after 2005. {2.2.2, 9.4.1; Box 9.2} In summary, the observed recent warming hiatus, defined as the reduction in GMST trend during 1998–2012 as compared to the trend during 1951–2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in external forcing (expert judgment, medium confidence). The forcing trend reduction is primarily due to a negative forcing trend from both volcanic eruptions and the downward phase of the solar cycle. However, there is low confidence in quantifying the role of forcing trend in causing the hiatus, because of uncertainty in the magnitude of the volcanic forcing trend and low confidence in the aerosol forcing trend. {Box 9.2}

Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus. There is medium confidence that the GMST trend difference between models and observations during 1998–2012 is to a substantial degree caused by internal variability, with possible contributions from forcing error and some CMIP5 models overestimating the response to increasing greenhouse-gas forcing. The CMIP5 model trend in effective radiative forcing (ERF) shows no apparent bias against the AR5 best estimate over 1998–2012. However, confidence in this assessment of CMIP5 ERF trend is low, primarily because of the uncertainties in model aerosol forcing and processes, which through spatial heterogeneity might well cause an undetected global-mean ERF trend error even in the absence of a trend in the global-mean aerosol loading. {Box 9.2}

The causes of both the observed GMST trend hiatus and of the model–observation GMST trend difference during 1998–2012 imply that, barring a major volcanic eruption, most 15-year GMST trends in the near-term future will be larger than during 1998–2012 (high confidence; see Section 11.3.6.3. for a full assessment of near-term projections of GMST). The reasons for this implication are fourfold: first, anthropogenic greenhouse-gas concentrations are expected to rise further in all RCP scenarios; second, anthropogenic aerosol concentration is expected to decline in all RCP scenarios, and so is the resulting cooling effect; third, the trend in solar forcing is expected to be larger over most near-term 15–year periods than over 1998–2012 (medium confidence), because 1998–2012 contained the full downward phase of the solar cycle; and fourth, it is more likely than not that internal climate variability in the near-term will enhance and not counteract the surface warming expected to arise from the increasing anthropogenic forcing. {Box 9.2}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , Box TS.3: Climate Models and the Hiatus in Global-Mean Surface Warming of the Past 15 years, p.61-63

TFE.4: The Changing Energy Budget of the Global Climate System

The global energy budget is a fundamental aspect of the Earth’s climate system and depends on many phenomena within it. The ocean has stored about 93% of the increase in energy in the climate system over recent decades resulting in ocean thermal expansion and hence sea level rise. The rate of storage of energy in the Earth system must be equal to the net downward radiative flux at the top of the atmosphere, which is the difference between effective radiative forcing due to changes imposed on the system, and the radiative response of the system. There are also significant transfers of energy between components of the climate system and from one location to another. The focus here is on the Earth’s global energy budget since 1970, when better global observational data coverage is available. {3.7, 9.4, 13.4; Box 3.1}

The effective radiative forcing of the climate system has been positive as a result of increases in well-mixed (long-lived) greenhouse gas concentrations, changes in short-lived greenhouse gases (tropospheric and stratospheric ozone and stratospheric water vapour), and an increase in solar irradiance (TFE.4, Figure 1a). This has been partly compensated by a negative contribution to the effective radiative forcing of the climate system as a result of changes in tropospheric aerosol, which predominantly reflect sunlight and furthermore enhance the brightness of clouds, although black carbon produces positive forcing. Explosive volcanic eruptions (such as El Chichón in Mexico in 1982 and Mount Pinatubo in the Philippines in 1991) can inject sulphur dioxide into the stratosphere, giving rise to stratospheric aerosol, which persists for several years. Stratospheric aerosol reflects some of the incoming solar radiation and thus gives a negative forcing. Changes in surface albedo from land-use change have also led to a greater reflection of shortwave radiation back to space and hence a negative forcing. Since 1970, the net effective radiative forcing of the climate system has increased, and the integrated impact of these forcings is an energy inflow over this period (TFE.4, Figure 1a). {2.3, 8.5; Box 13.1}

As the climate system warms, energy is lost to space through increased outgoing radiation. This radiative response by the system is predominantly due to increased thermal radiation, but it is modified by climate feedbacks such as changes in water vapour, clouds, and surface albedo, which affect both outgoing long- wave and reflected shortwave radiation. The top of the atmosphere fluxes have been measured by the Earth Radiation Budget Experiment (ERBE) satellites from 1985 to 1999 and the Cloud and the Earth’s Radiant Energy System (CERES) satellites from March 2000 to the present. The top of the atmosphere radiative flux measurements are highly precise, allowing identification of changes in the Earth’s net energy budget from year to year within the ERBE and CERES missions, but the absolute calibration of the instruments is not sufficiently accurate to allow determination of the absolute top of the atmosphere energy flux or to provide continuity across missions. TFE.4, Figure 1b relates the cumulative total energy change of the Earth system to the change in energy storage and the cumulative outgoing radiation. Calculation of the latter is based on the observed global-mean surface temperature multiplied by the climate feedback parameter α, which in turn is related to the equilibrium climate sensitivity. The mid-range value for α, 1.23 W m–2 °C–1, corresponds to an effective radiative forcing for a doubled CO2 concentration of 3.7 [2.96 to 4.44] W m–2 combined with an equilibrium climate sensitivity of 3.0°C. The climate feedback parameter α is likely to be in the range from 0.82 W m–2 °C–1 to 2.47 W m–2 °C–1 (corresponding to the likely range in equilibrium climate sensitivity of 1.5°C to 4.5°C). {9.7.1, Box 12.2} If effective radiative forcing were fixed, the climate system would eventually warm sufficiently that the radiative response would balance the effective radiative forcing, and there would be no further change in energy storage in the climate system. However, the forcing is increasing, and the ocean’s large heat capacity means that the climate system is not in radiative equilibrium and its energy content is increasing (TFE.4, Figure 1b). This storage provides strong evidence of a changing climate. The majority of this additional heat is in the upper 700 m of the ocean, but there is also warming in the deep and abyssal ocean. The associated thermal expansion of the ocean has contributed about 40% of the observed sea level rise since 1970. A small amount of additional heat has been used to warm the continents, warm and melt glacial and sea ice, and warm the atmosphere. {13.4.2; Box 3.1, Box 13.1}

In addition to these forced variations in the Earth’s energy budget, there is also internal variability on decadal time scales. Observations and models indicate that, because of the comparatively small heat capacity of the atmosphere, a decade of steady or even decreasing surface temperature can occur in a warming world. Climate model simulations suggest that these periods are associated with a transfer of heat from the upper to the deeper ocean, of the order 0.1 W m–2, with a near-steady or an increased radiation to space, again of the order 0.1 W m–2. While these natural fluctuations represent a large amount of heat, they are significantly smaller than the anthropogenic forcing of the Earth’s energy budget, particularly on timescales of several decades or longer {9.4; Box 9.2, 13.1}.

The available independent estimates of effective radiative forcing, of observed heat storage, and of surface warming combine to give an energy budget for the Earth that is consistent with the assessed likely range of equilibrium climate sensitivity to within estimated uncertainties. Quantification of the terms in the Earth’s energy budget and verification that these terms balance over recent decades provides strong evidence for our understanding of anthropogenic climate change {Box 13.1}.

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TFE.4: The Changing Energy Budget of the Global Climate System, p.67-68

4.3 How do projections from previous assessments compare with the present observations?

The source document for this Digest states:

TFE.3: Comparing Projections from Previous IPCC Assessments with Observations Verification of projections is arguably the most convincing way of establishing the credibility of climate change science. Results of projected changes in CO2 , global mean surface temperature and global mean sea level from previous IPCC assessment reports are quantitatively compared with the best available observational estimates. The comparison between the four previous reports highlights the evolution in our understanding of how the climate system responds to changes in both natural and anthropogenic forcing and provides an assessment of how the projections compare with observational estimates. TFE.3, Figure 1, for example, shows the projected and observed estimates of: (i) CO2 changes (top row), (ii) global mean surface temperature anomaly relative to 1961–1990 (middle row) and (iii) global mean sea level relative to 1990 (bottom row). Results from previous assessment reports are in the left hand column, and for completeness results from current assessment are given in the right hand column. {2.4, 3.7, 6.3, 11.3, 13.3}

CO2 Changes From 1950–2011 the observed concentrations of atmospheric CO2 have steadily increased. Considering the period 1990–2011, the observed CO2 concentration changes lie within the envelope of the scenarios used in the four assessment reports. As the most recent assessment, AR4 (TFE.3.Figure 1; top left) has the narrowest scenario range and the observed concentration follows this range. The AR5 results (TFE.3.Figure 1; top right) are consistent with AR4, and during 2002–2011, atmospheric CO2 concentrations increased at a rate of 1.9 to 2.1 ppm yr–1. {2.2.1, 6.3; Table 6.1}

Global Mean Temperature Anomaly

Relative to the 1961–1990 mean, the global mean surface temperature anomaly has been positive and larger than 0.25°C since 2001. Overall the observed temperature record lies within the total range of uncertainty (i.e., the combined effects of scenario uncertainty, observational uncertainty and uncertainty due to natural variability; area enclosed colored wedges (TFE.3, Figure1, middle left: FAR, SAR, TAR, AR4: B1, A1B, A2) and by individual CMIP3 simulations and projections). This is also true for the CMIP5 results (TFE.3, Figure 1; middle right) in the sense that the observed record lies within the range of the model projections, but on the lower end of the plume. Mount Pinatubo erupted in 1991 (see FAQ 11.2 for discussion of how volcanoes impact the climate system) leading to a brief period of relative global mean cooling during the early 1990’s. The FAR, SAR and TAR did not include the effects of volcanic eruptions and thus failed to include the cooling associated with the Pinatubo eruption. AR4 and AR5, however, did include the effects from volcanoes and did simulate successfully the associated cooling. During 1995–2000 the global mean temperature anomaly was quite variable – a significant fraction of this variability was due to the large El Niño in 1997–1998 and the strong back-to-back La Niña’s in 1999–2001. The projections associated with these assessment reports do not attempt to capture the actual evolution of these El Niño and La Niña events, but includes them as a source of uncertainty due to natural variability as encompassed by, for example, the range given by the individual CMIP3 and CMIP5 simulations and projection (TFE.3, Figure 1). The grey wedge in TFE.3, Figure 1 (middle right) corresponds to the indicative likely range for annual temperatures, which is determined from the all RCPs assessed value for the 20-year mean 2016–2035 (see discussion of Figure TS.14 and Section 11.3.6 for details). From 1998–2012 the observational estimates have largely been on the low end of the range given by the scenarios alone in previous assessment reports and CMIP3 and CMIP5 projections. {2.4; Box 9.2}

Global Mean Sea Level

Based on both tide gauge and satellite altimetry data, relative to 1990, the global mean sea level has continued to rise. While the increase is fairly steady, both observational records show short periods of either no change or a slight decrease. The observed estimates lie within the envelope of all the projections except perhaps in the very early 1990s. The sea level rise uncertainty due to scenario-related uncertainty is smallest for the most recent assessments (AR4 and AR5) and observed estimates lie well within this scenario-related uncertainty. It is virtually certain that over the 20th century that sea level rose. The mean rate of sea level increase was 1.7 mm yr–1 with a very likely range between 1.5 to 1.9 between 1901 and 2010 and this rate increased to 3.2 with a likely range of 2.8 to 3.6 mm yr–1 between 1993 and 2010 (see TFE.2). {3.7.2, 3.7.4}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TFE.3: Comparing Projections from Previous IPCC Assessments with Observations, p.64-65

4.4 How have the oceans changed?

The source document for this Digest states:

TS.4.4 Oceans

The observed upper-ocean warming during the late 20th and early 21st centuries and its causes have been assessed more completely since AR4 using updated observations and more simulations (see Section TS.2.2). The long term trends and variability in the observations are most consistent with simulations of the response to both anthropogenic forcing and volcanic forcing. The anthropogenic fingerprint in observed upper-ocean warming, consisting of global mean and basin-scale pattern changes, has also been detected. This result is robust to a number of observational, model and methodological or structural uncertainties. It is very likely that anthropogenic forcings have made a substantial contribution to upper ocean warming (above 700m) observed since the 1970s. This anthropogenic ocean warming has contributed to global sea level rise over this period through thermal expansion. {3.2.2, 3.2.3, 3.7.2, 10.4.1, 10.4.3; Box 3.1}

Observed surface salinity changes also suggest a change in the global water cycle has occurred (see TFE.1). The long term trends show that there is a strong positive correlation between the mean climate of the surface salinity and the temporal changes of surface salinity from 1950 to 2000. This correlation shows an enhancement of the climatological salinity pattern—so fresh areas have become fresher and salty areas saltier. The strongest anthropogenic signals are in the tropics (TRO, 30°S–30°N) and the Western Pacific. The salinity contrast between the Pacific and Atlantic oceans is also increased with significant contributions from anthropogenic forcing. {3.3, 10.3.2, 10.4.2; FAQ 3.3}

On a global scale, surface and subsurface salinity changes (1955–2004) over the upper 250m of the water column are very unlikely to be explained by natural variability. However, the observed salinity changes match the modelled distribution of forced changes (greenhouse gases and tropospheric aerosols). Natural external variability taken from the simulations with just the variations in solar and volcanic forcing do not match the observations at all, thus excluding the hypothesis that observed trends can be explained by just solar or volcanic variations. These lines of evidence and our physical understanding of the physical processes leads to the conclusions that it is very likely that anthropogenic forcings have made a discernable contribution to surface and subsurface oceanic salinity changes since the 1960's. {10.4.2; Table 10.1} Oxygen is an important physical and biological tracer in the ocean. Global analyses of oxygen data from the 1960’s to 1990’s extend the spatial coverage from local to global scales and have been combined with attribution studies for a limited range of earth system models. It is concluded that there is medium confidence that the observed global pattern of decrease in dissolved oxygen in the oceans can be attributed in part to human influences. {3.8.3, 10.4.4; Table 10.1}

The observations show distinct trends for ocean acidification (which is observed to be between –0.0014 and –0.0024 pH units per year). It is very likely that oceanic uptake of anthropogenic carbon dioxide has resulted in the acidification of surface waters. {3.8.2, 10.4.4; Box 3.2; Table 10.1}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TS.4.4 Oceans, p.68-69

4.5 Snow and Ice

The source document for this Digest states:

TS.4.5 Cryosphere

The reductions in Arctic sea ice extent and northern hemisphere snow cover extent and widespread glacier retreat and increased surface melt of Greenland (see TS.2) are all evidence of systematic changes in the cryosphere. All of these changes in the cryosphere have been linked to anthropogenic forcings. {4.2.2, 4.4, 4.5, 4.6, 10.5.1, 10.5.3; Table 10.1}

Attribution studies, comparing the seasonal evolution of Arctic sea ice extent from observations from the 1950s with those simulated by coupled model simulations, demonstrate that human influence on the sea ice extent changes can be robustly detected since the early 1990s. The anthropogenic signal is also detectable for individual months from May to December, suggesting that human influence, strongest in late summer, now also extends into colder seasons. From these simulations of sea-ice and observed sea-ice extent from the instrumental record with high agreement between studies, it is concluded that anthropogenic forcings are very likely to have contributed to Arctic sea ice loss since 1979 (Figure TS.12). {10.5.1}

For Antarctic sea ice extent, the shortness of the observed record and differences in simulated and observed variability preclude an assessment of whether or not the observed increase since 1979 is inconsistent with internal variability. Untangling the processes involved with trends and variability in Antarctica and surrounding waters remains complex and several studies are contradictory. In conclusion there is low confidence in the scientific understanding of the observed increase in Antarctic sea ice extent since 1979, due to the large differences between sea-ice simulations from CMIP5 models and to the incomplete and competing scientific explanations for the causes of change and low confidence in estimates of internal variability (Figure TS.12). {9.4.3, 10.5.1; Table 10.1}

The Greenland ice sheet shows recent major melting episodes in response to record temperatures relative to the 20th century associated with persistent shifts in early summer atmospheric circulation, and these shifts have become more pronounced since 2007. While many Greenland instrumental records are relatively short (two decades), regional modelling and observations tell a consistent story of the response of Greenland temperatures and ice sheet runoff to shifts in regional atmospheric circulation associated with larger scale flow patterns and global temperature increases. Mass loss and melt is also occurring in Greenland through the intrusion of warm water into the major fjords containing glaciers such as Jacobshaven Glacier. It is likely that anthropogenic forcing has contributed to surface melting of the Greenland ice sheet since 1990. {10.5.2; Table 10.1}

Estimates of ice mass in Antarctica since 2000 show that the greatest losses are at the edges. An analysis of observations underneath a floating ice shelf off West Antarctica leads to the conclusion that ocean warming in this region and increased transport of heat by ocean circulation are largely responsible for accelerating melt rates. The observational record of Antarctic mass loss is short and the internal variability of the ice sheet is poorly understood. These factors combined with incomplete models of Antarctic ice sheet mass loss result in low confidence in scientific understanding of this mass loss, and consequently the attribution of the decrease in mass of Antarctic ice sheet to human influence is premature. {3.2, 4.2, 4.4.3, 10.5.2}

The evidence for the retreat of glaciers due to warming and moisture change is now more complete than at the time of AR4. There is high confidence in the estimates of observed mass loss and the estimates of natural variations and internal variability from long term glacier records. Based on these factors and our understanding of glacier response to climatic drivers there is high confidence that a substantial part of the mass loss of glaciers is likely due to human influence. It is likely that there has been an anthropogenic component to observed reductions in northern hemisphere snow cover since 1970. {4.3.3, 10.5.2, 10.5.3; Table 10.1}

TS.4.6 Water Cycle

Since the AR4, new evidence has emerged of a detectable human influence on several aspects of the water cycle. There is medium confidence that observed changes in near-surface specific humidity since 1973 contain a detectable anthropogenic component. The anthropogenic water vapour fingerprint simulated by an ensemble of climate models has been detected in lower tropospheric moisture content estimates derived from SSM/I data covering the period 1988–2006. An anthropogenic contribution to increases in tropospheric specific humidity is found with medium confidence. {2.5, 10.3}

Attribution studies of global zonal mean terrestrial precipitation and Arctic precipitation both find a detectable anthropogenic influence. Overall there is medium confidence in a significant human influence on global scale changes in precipitation patterns, including increases in northern hemisphere mid to high latitudes. Remaining observational and modeling uncertainties and the large effect of internal variability on observed precipitation preclude a more confident assessment. {2.5, 7.6, 10.3}

Based on the collected evidence for attributable changes (with varying levels of confidence and likelihood) in specific humidity, terrestrial precipitation, and ocean surface salinity through its connection to precipitation and evaporation, and from physical understanding of the water cycle, it is likely that human influence has affected the global water cycle since 1960. This is a major advance since AR4. {2.4, 2.5, 3.3, 9.4.1, 10.3, 10.4.2; Table 10.1; FAQ 3.3}

TS.4.7 Climate Extremes

Several new attribution studies have found a detectable anthropogenic influence in the observed increased frequency of warm days and nights and decreased frequency of cold days and nights. Since the AR4 and SREX, there is new evidence for detection of human influence on extremely warm daytime temperature and there is new evidence that the influence of anthropogenic forcing may be detected separately from the influence of natural forcing at global scales and in some continental and sub-continental regions. This strengthens the conclusions from both AR4 and SREX, and it is now very likely that anthropogenic forcing has contributed to the observed changes in the frequency and intensity of daily temperature extremes on the global scale since the mid-20th century. It is likely that human influence has significantly increased the probability of occurrence of heat waves in some locations. See TFE.9 and TFE.9, Table 1 for a summary of the assessment of extreme weather and climate events. {10.6}

Since the AR4, there is some new limited direct evidence for an anthropogenic influence on extreme precipitation, including a formal detection and attribution study and indirect evidence that extreme precipitation would be expected to have increased given the evidence of anthropogenic influence on various aspects of the global hydrological cycle and high confidence that the intensity of extreme precipitation events will increase with warming, at a rate well exceeding that of the mean precipitation. . In land regions where observational coverage is sufficient for assessment, there is medium confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. {7.6, 10.6}

Globally, there is low confidence in attribution of changes in tropical cyclone activity to human influence. This is due to insufficient observational evidence, lack of physical understanding of the links between anthropogenic drivers of climate and tropical cyclone activity, and the low level of agreement between studies as to the relative importance of internal variability, and anthropogenic and natural forcings. In the North Atlantic region there is medium confidence that a reduction in aerosol forcing over the North Atlantic has contributed at least in part to the observed increase in tropical cyclone activity there since the 1970s. There remains substantial disagreement on the relative importance of internal variability, greenhouse gas forcing, and aerosols for this observed trend. {2.6, 10.6, 14.6}

While the AR4 concluded that it is more likely than not that anthropogenic influence has contributed to an increased risk of drought in the second half of the 20th century, an updated assessment of the observational evidence indicates that the AR4 conclusions regarding global increasing trends in hydrological droughts since the 1970s are no longer supported. Owing to the low confidence in observed large-scale trends in dryness combined with difficulties in distinguishing decadal-scale variability in drought from long-term climate change, there is now low confidence in the attribution of changes in drought over global land since the mid-20th century to human influence. {2.6, 10.6}

TS.4.8 From Global to Regional

Taking a longer term perspective shows the substantial role played by external forcings in driving climate variability on hemispheric scales in pre-industrial times (Box TS.5). It is very unlikely that Northern Hemisphere temperature variations from 1400 to 1850 can be explained by internal variability alone. There is medium confidence that external forcing contributed to Northern Hemispheric temperature variability from 850 to 1400 and that external forcing contributed to European temperature variations over the last 5 centuries. {5.3.3, 5.5.1, 10.7.2, 10.7.5; Table 10.1}

Changes in atmospheric circulation are important for local climate change since they could lead to greater or smaller changes in climate in a particular region than elsewhere. It is likely that human influence has altered sea level pressure patterns globally. There is medium confidence that stratospheric ozone depletion has contributed to the observed poleward shift of the southern Hadley Cell border during Austral summer. It is likely that stratospheric ozone depletion has contributed to the positive trend in the Southern Annular Mode seen in Austral summer since the mid-20th century which corresponds to sea level pressure reductions over the high latitudes and increase in the subtropics (Figure TS.11). {10.3}

The evidence is stronger that observed changes in the climate system can now be attributed to human activities on global and regional scales in many components (Figure TS.12). Observational uncertainty has been explored much more thoroughly than previously, and fingerprints of human influence have been deduced from a new generation of climate models. There is improved understanding of ocean changes, including salinity changes, that are consistent with large scale intensification of the water cycle predicted by climate models. The changes in near surface temperatures, free atmosphere temperatures, ocean temperatures, and Northern Hemisphere snow cover and sea ice extent, when taken together, show not just global mean changes, but distinctive regional patterns consistent with the expected fingerprints of change from anthropogenic forcings and the expected responses from volcanic eruptions (Figure TS.12). {10.3– 10.6, 10.9}

Human influence has been detected in nearly all of the major assessed components of the climate system (Figure TS.12). Taken together, the combined evidence increases the overall level of confidence in the attribution of observed climate change, and reduces the uncertainties associated with assessment based on a single climate variable. From this combined evidence it is virtually certain that human influence has warmed the global climate system. Anthropogenic influence has been identified in changes in temperature near the surface of the earth, in the atmosphere and in the oceans, as well as changes in the cryosphere, the water cycle and some extremes. There is strong evidence that excludes solar forcing, volcanoes, and internal variability as the strongest drivers of warming since 1950. {10.9; Table 10.1; FAQ 5.1}

Over every continent except Antarctica, anthropogenic influence has likely made a substantial contribution to surface temperature increases since the mid-20th century (Figure TS.12). It is likely that there has been significant anthropogenic contribution to the very substantial warming in Arctic land surface temperatures over the past 50 years. For Antarctica large observational uncertainties result in low confidence that anthropogenic influence has contributed to observed warming averaged over available stations. Detection and attribution at regional scales is complicated by the greater role played by dynamical factors (circulation changes), a greater range of forcings that may be regionally important, and the greater difficulty of modelling relevant processes at regional scales. Nevertheless, human influence has likely contributed to temperature increases in many sub-continental regions. {10.3, Box 5.1}

The coherence of observed changes with simulations of anthropogenic and natural forcing in the physical system is remarkable (Figure TS.12), particularly for temperature related variables. Surface temperature and ocean heat content show emerging anthropogenic and natural signals in both records, and a clear separation from the alternative hypothesis of just natural variations. These signals do not just appear in the global means, but also appear at regional scales on continents and in ocean basins in each of these variables. Sea-ice extent emerges clearly from the range of internal variability for the Arctic. At sub-continental scales human influence is likely to have substantially increased the probability of occurrence of heat waves in some locations. {Table 10.1}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TS.4.5-TS.4.8, p.68-74

4.6 What are the main climate changes that could be irreversible?

    • 4.6.1 Irreversible changes in ocean circulation
    • 4.6.2 Irreversible changes in release of the permafrost
    • 4.6.3 Irreversible change in Collapse of ice sheets

The source document for this Digest states:

TFE.5: Irreversibility and Abrupt Change

A number of components or phenomena within the climate system have been proposed as potentially exhibiting threshold behaviour. Crossing such thresholds can lead to an abrupt or irreversible transition into a different state of the climate system or some of its components. Abrupt climate change is defined in AR5 as a large-scale change in the climate system that takes place over a few decades or less, persists (or is anticipated to persist) for at least a few decades, and causes substantial disruptions in human and natural systems. There is information on potential consequences of some abrupt changes, but in general there is low confidence and little consensus on the likelihood of such events over the 21st century. Examples of components susceptible to such abrupt change are the strength of the Atlantic Meridional Overturning Circulation (AMOC), clathrate methane release, tropical and boreal forest dieback, disappearance of summer sea ice in the Arctic Ocean, long-term drought and monsoonal circulation. {5.7, 6.4.7, 12.5.5; Table 12.4}

A change is said to be irreversible if the recovery timescale from this state due to natural processes is significantly longer than the time it takes for the system to reach this perturbed state. Such behaviour may arise because the timescales for perturbations and recovery processes are different, or because climate change may persist due to the long residence time of a CO2 perturbation in the atmosphere (see TFE.8). While changes in Arctic- Ocean summer sea-ice extent, long-term droughts and monsoonal circulation are assessed to be reversible within years to decades, tropical or boreal forest dieback may only be reversible within centuries. Changes in clathrate methane and permafrost carbon release, Greenland and Antarctic ice sheet collapse may be irreversible during millennia after the causal perturbation. {5.8, 6.4.7, 12.5.5, 13.4.3, 13.4.4; Table 12.4}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TFE.5: Irreversibility and Abrupt Change, p.70-72

4.6.1 Irreversible changes in ocean circulation

The source document for this Digest states:

Abrupt Climate Change Linked with AMOC

New transient climate model simulations have confirmed with high confidence that strong changes in the strength of the AMOC produce abrupt climate changes at global scale with magnitude and pattern resembling past glacial Dansgaard-Oeschger events and Heinrich stadials. Confidence in the link between changes in North Atlantic climate and low-latitude precipitation has increased since AR4. From new paleoclimate reconstructions and modelling studies, there is very high confidence that a reduced strength of the AMOC and the associated surface cooling in the North Atlantic region caused southward shifts of the Atlantic innertropical convergence zoneand affected the American (north and south), African and Asian monsoons. {5.7}

The interglacial mode of the AMOC can recover (high confidence) from a short-lived freshwater input into the subpolar North Atlantic. Approximately 8,200 years ago, a marked reduction in the strength of the AMOC was driven by a sudden freshwater release in the final stages of North American ice sheet melting.

Paleoclimate observations and model results indicate, with high confidence, that the circulation was restored within approximately 200 years after the perturbation. {5.8.1} While many more model simulations have been conducted since AR4 under a wide range of future forcing scenarios, projections of the AMOC behaviour have not changed. It remains very likely that the AMOC will weaken over the 21st century relative to preindustrial values with a best estimate decrease in 2100 of about 20–30% for the RCP4.5 scenario and 36–44% for the RCP8.5 scenario, but there is low confidence on the magnitude of weakening. It also remains very unlikely that the AMOC will undergo an abrupt transition or collapse in the 21st century for the scenarios considered (high confidence) (TFE.5, Figure 1). For an abrupt transition of the AMOC to occur, the sensitivity of the AMOC to forcing would have to be far greater that seen in current models, or would require meltwater flux from the Greenland ice sheet greatly exceeding even the highest of current projections. While neither possibility can be excluded entirely, it is unlikely that the AMOC will collapse beyond the end of the 21st century for the scenarios considered but a collapse beyond the 21st century for large sustained warming cannot be excluded. There is low confidence in assessing the evolution of AMOC beyond the 21st century because of limited number of analyses and equivocal results.{12.4.7, 12.5.5}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TFE.5: Irreversibility and Abrupt Change, p.70-72

4.6.2 Irreversible changes in release of the permafrost

The source document for this Digest states:

Potential Irreversibility of Changes in Permafrost, Methane Clathrates and Forests In a warming climate, permafrost thawing may induce decomposition of carbon accumulated in frozen soils which could persist for hundreds to thousands of years, leading to an increase of atmospheric CO2 and/or CH4 concentrations. The existing modelling studies of permafrost carbon balance under future warming that take into account at least some of the essential permafrost-related processes do not yield consistent results, beyond the fact that present-day permafrost will become a net emitter of carbon during the 21st century under plausible future warming scenarios (low confidence). This also reflects an insufficient understanding of the relevant soil processes during and after permafrost thaw, including processes leading to stabilization of unfrozen soil carbon, and precludes any quantitative assessment of the amplitude of irreversible changes in the climate system potentially related to permafrost degassing and associated feedbacks. {6.4.7, 12.5.5}

Anthropogenic warming will very likely lead to enhanced methane emissions from both terrestrial and oceanic clathrates. Deposits of methane clathrates below the sea floor are susceptible to destabilization via ocean warming. However, sea level rise due to changes in ocean mass enhances clathrate stability in the ocean. While difficult to formally assess, initial estimates of the 21st century feedback from methane clathrate destabilization are small but not insignificant. It is very unlikely that methane from clathrates will undergo catastrophic release during the 21st century (high confidence). On multi-millennial timescales, such methane emissions may provide a positive feedback to anthropogenic warming and may be irreversible, due to the difference between release and accumulation timescales. {6.4.7, 12.5.5} The existence of critical climate change driven dieback thresholds in the Amazonian and other tropical rainforests purely driven by climate change remains highly uncertain. The possibility of a critical threshold being crossed in precipitation volume and duration of dry seasons cannot be ruled out. The response of boreal forest to projected climate change is also highly uncertain, and the existence of critical thresholds cannot at present be ruled out. There is low confidence in projections of the collapse of large areas of tropical and/or boreal forests. {12.5.5}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TFE.5: Irreversibility and Abrupt Change, p.70-72

4.6.3 Irreversible change in Collapse of ice sheets

The source document for this Digest states:

Potential Irreversibility of Changes in the Cryosphere

The reversibility of sea ice loss has been directly assessed in sensitivity studies to CO2 increase and decrease with AOGCMs or ESMs. None of them show evidence of an irreversible change in Arctic sea ice at any point. By contrast, as a result of the strong coupling between surface and deep waters in the Southern Ocean, the Antarctic sea ice in some models integrated with ramp-up and ramp-down atmospheric CO2 concentration exhibits some hysteresis behaviour. {12.5.5}

At present, both the Greenland and Antarctic ice sheets have a positive surface mass balance (snowfall exceeds melting), although both are losing mass because ice outflow into the sea exceeds the net surface mass balance. A positive feedback operates to reduce ice sheet volume and extent when a decrease of the surface elevation of the ice sheet induces a decreased surface mass balance. This arises generally through increased surface melting, and therefore applies in the 21st century to Greenland, but not to Antarctica, where surface melting is currently very small. Surface melting in Antarctica is projected to become important after several centuries under high WMGHG radiative forcing scenarios. {4.4, 13.4.4; Box 5.2, 13.2}

Abrupt change in ice-sheet outflow to the sea may be caused by unstable retreat of the grounding line in regions where the bedrock is below sea level and slopes downwards towards the interior of the ice sheet. This mainly applies to West Antarctica, but also to parts of East Antarctica and Greenland. Grounding line retreat can be triggered by ice-shelf decay, due to warmer ocean water under ice shelves enhancing submarine ice-shelf melt, or melt water ponds on the surface of the ice shelf promoting ice-shelf fracture.

Because ice sheet growth is a slow process, such changes would be irreversible in the definition adopted here. {4.4.5, Box 13.2}

There is high confidence that the volumes of the Greenland and West Antarctic ice sheets were reduced during periods of the past few million years that were globally warmer than present. Ice sheet model simulations and geological data suggest that the West Antarctic ice sheet is very sensitive to subsurface ocean warming and imply with medium confidence a West Antarctic ice sheet retreat if atmospheric CO2 concentration remains above approximately 400 ppm for several millennia. {5.8, 13.4.4; Box 13.2}

The available evidence indicates that global warming beyond a threshold would lead to the near-complete loss of the Greenland Ice Sheet over a millennium or longer, causing a global mean sea level rise of approximately 7 m. Studies with fixed present-day ice sheet topography indicate that the threshold is greater than 2°C but less than 4°C of global mean surface temperature rise with respect to preindustrial. Taking into account the increased vulnerability of the ice sheet as the surface elevation decreases due to the loss of ice, a study with a dynamical ice sheet suggests the threshold could be as low as 1°C. Considering the present state of scientific uncertainty, a likely range cannot be quantified. The complete loss of the Greenland ice sheet is not inevitable because this would take a millennium or more; if temperatures decline before the ice sheet has completely vanished, the ice sheet might regrow. However, some part of the mass loss might be irreversible, depending on the duration and degree of exceedance of the threshold, because the ice sheet may have multiple steady states, due to its interaction with regional climate. {13.4.3, 13.4.4}

Source & ©: IPCC  Working Group I Contribution To The IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis. , TFE.5: Irreversibility and Abrupt Change, p.70-72


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