Global surface air temperatures in CMIP6: Historical performance and future

2020 ◽  
Author(s):  
Xuewei Fan
Keyword(s):  
21St Century ◽  
Land Surface ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Global Climate ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Global Climate Models ◽  
Global Land ◽  
Model Ensembles

<p>Surface air temperature outputs from 16 global climate models (GCMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901–2014. Projections of Bayesian model averaging (BMA) multi-model ensembles under four different Shared Socioeconomic Pathways (SSPs) were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial changes in observed temperature with a pattern correlation typically greater than 0.98. However, most models underestimate annual temperature over northeastern North America and overestimate it over central Eurasia. In addition, most CMIP6 models overestimate the warming trend in most regions. The BMA multi-model ensembles show more agreement than individual models do in simulating the spatial patterns of the temperature, but with less spatial variability compared with the observations. In the 21st century, temperature is generally projected to increase over the global land surface under all four SSP scenarios. By the end of the 21st century, temperature is projected to increase by 1.35 °C/100 yr, 3.61 °C/100 yr, 6.39 °C/100 yr and 8.03 °C/100 yr under the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, respectively, with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere.</p>

scholarly journals Model improvement and future projection of permafrost processes in a global land surface model

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tokuta Yokohata ◽  
Kazuyuki Saito ◽  
Kumiko Takata ◽  
Tomoko Nitta ◽  
Yusuke Satoh ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Global Climate ◽  
Land Surface Model ◽  
Global Climate Models ◽  
Soil Freezing ◽  
Surface Model ◽  
Southern Limit ◽  
Future Projections ◽  
Global Land

AbstractTo date, the treatment of permafrost in global climate models has been simplified due to the prevailing uncertainties in the processes involving frozen ground. In this study, we improved the modeling of permafrost processes in a state-of-the-art climate model by taking into account some of the relevant physical properties of soil such as changes in the thermophysical properties due to soil freezing. As a result, the improved version of the global land surface model was able to reproduce a more realistic permafrost distribution at the southern limit of the permafrost area by increasing the freezing of soil moisture in winter. The improved modeling of permafrost processes also had a significant effect on future projections. Using the conventional formulation, the predicted cumulative reduction of the permafrost area by year 2100 was approximately 60% (40–80% range of uncertainty from a multi-model ensemble) in the RCP8.5 scenario, while with the improved formulation, the reduction was approximately 35% (20–50%). Our results indicate that the improved treatment of permafrost processes in global climate models is important to ensuring more reliable future projections.

scholarly journals LGM permafrost distribution: how well can the latest PMIP multi-model ensembles reconstruct?

2013 ◽  
Vol 9 (2) ◽  
pp. 1565-1597 ◽  
Author(s):  
K. Saito ◽  
T. Sueyoshi ◽  
S. Marchenko ◽  
V. Romanovsky ◽  
B. Otto-Bliesner ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Direct Method ◽  
Surface Air Temperature ◽  
Global Scale ◽  
Global Climate Models ◽  
Phase Iii ◽  
Frozen Ground ◽  
Soil Temperatures ◽  
Model Ensembles

Abstract. Global-scale frozen ground distribution during the Last Glacial Maximum (LGM) was reconstructed using multi-model ensembles of global climate models, and then compared with evidence-based knowledge and earlier numerical results. Modeled soil temperatures, taken from Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) simulations, were used to diagnose the subsurface thermal regime and determine underlying frozen ground types for the present-day (pre-industrial; 0 k) and the LGM (21 k). This direct method was then compared to the earlier indirect method, which categorizes the underlying frozen ground type from surface air temperature, applied to both the PMIP2 (phase II) and PMIP3 products. Both direct and indirect diagnoses for 0 k showed strong agreement with the present-day observation-based map, although the soil temperature ensemble showed a higher diversity among the models partly due to varying complexity of the implemented subsurface processes. The area of continuous permafrost estimated by the multi-model analysis was 25.6 million km2 for LGM, in contrast to 12.7 million km2 for the pre-industrial control, whereas seasonally, frozen ground increased from 22.5 million km2 to 32.6 million km2. These changes in area resulted mainly from a cooler climate at LGM, but other factors as well, such as the presence of huge land ice sheets and the consequent expansion of total land area due to sea-level change. LGM permafrost boundaries modeled by the PMIP3 ensemble-improved over those of the PMIP2 due to higher spatial resolutions and improved climatology-also compared better to previous knowledge derived from the geomorphological and geocryological evidences. Combinatorial applications of coupled climate models and detailed stand-alone physical-ecological models for the cold-region terrestrial, paleo-, and modern climates will advance our understanding of the functionality and variability of the frozen ground subsystem in the global eco-climate system.

scholarly journals LGM permafrost distribution: how well can the latest PMIP multi-model ensembles perform reconstruction?

2013 ◽  
Vol 9 (4) ◽  
pp. 1697-1714 ◽  
Author(s):  
K. Saito ◽  
T. Sueyoshi ◽  
S. Marchenko ◽  
V. Romanovsky ◽  
B. Otto-Bliesner ◽  
...  
Keyword(s):  
Indirect Method ◽  
Climate Models ◽  
Global Climate ◽  
Direct Method ◽  
Surface Air Temperature ◽  
Global Scale ◽  
Global Climate Models ◽  
Phase Iii ◽  
Frozen Ground ◽  
Model Ensembles

Abstract. Here, global-scale frozen ground distribution from the Last Glacial Maximum (LGM) has been reconstructed using multi-model ensembles of global climate models, and then compared with evidence-based knowledge and earlier numerical results. Modeled soil temperatures, taken from Paleoclimate Modelling Intercomparison Project phase III (PMIP3) simulations, were used to diagnose the subsurface thermal regime and determine underlying frozen ground types for the present day (pre-industrial; 0 kya) and the LGM (21 kya). This direct method was then compared to an earlier indirect method, which categorizes underlying frozen ground type from surface air temperature, applying to both the PMIP2 (phase II) and PMIP3 products. Both direct and indirect diagnoses for 0 kya showed strong agreement with the present-day observation-based map. The soil temperature ensemble showed a higher diversity around the border between permafrost and seasonally frozen ground among the models, partly due to varying subsurface processes, implementation, and settings. The area of continuous permafrost estimated by the PMIP3 multi-model analysis through the direct (indirect) method was 26.0 (17.7) million km2 for LGM, in contrast to 15.1 (11.2) million km2 for the pre-industrial control, whereas seasonally frozen ground decreased from 34.5 (26.6) million km2 to 18.1 (16.0) million km2. These changes in area resulted mainly from a cooler climate at LGM, but from other factors as well, such as the presence of huge land ice sheets and the consequent expansion of total land area due to sea-level change. LGM permafrost boundaries modeled by the PMIP3 ensemble – improved over those of the PMIP2 due to higher spatial resolutions and improved climatology – also compared better to previous knowledge derived from geomorphological and geocryological evidence. Combinatorial applications of coupled climate models and detailed stand-alone physical-ecological models for the cold-region terrestrial, paleo-, and modern climates will advance our understanding of the functionality and variability of the frozen ground subsystem in the global eco-climate system.

scholarly journals Projected 21st century changes in snow water equivalent over Northern Hemisphere landmasses from the CMIP5 model ensemble

2015 ◽  
Vol 9 (5) ◽  
pp. 1943-1953 ◽  
Author(s):  
H. X. Shi ◽  
C. H. Wang
Keyword(s):  
Northern Hemisphere ◽  
21St Century ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
The Tibetan Plateau ◽  
Model Ensemble ◽  
Snow Water

Abstract. Changes in snow water equivalent (SWE) over Northern Hemisphere (NH) landmasses are investigated for the early (2016–2035), middle (2046–2065) and late (2080–2099) 21st century using a multi-model ensemble from 20 global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The multi-model ensemble was found to provide a realistic estimate of observed NH mean winter SWE compared to the GlobSnow product. The multi-model ensemble projects significant decreases in SWE over the 21st century for most regions of the NH for representative concentration pathways (RCPs) 2.6, 4.5 and 8.5. This decrease is particularly evident over the Tibetan Plateau and North America. The only region with projected increases is eastern Siberia. Projected reductions in mean annual SWE exhibit a latitudinal gradient with the largest relative changes over lower latitudes. SWE is projected to undergo the largest decreases in the spring period where it is most strongly negatively correlated with air temperature. The reduction in snowfall amount from warming is shown to be the main contributor to projected changes in SWE during September to May over the NH.

Future Changes in Northern Hemisphere Snowfall

2013 ◽  
Vol 26 (20) ◽  
pp. 7813-7828 ◽  
Author(s):  
John P. Krasting ◽  
Anthony J. Broccoli ◽  
Keith W. Dixon ◽  
John R. Lanzante
Keyword(s):  
Twentieth Century ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Noise Analysis ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Global Climate Models ◽  
First Century ◽  
Twenty First Century

Abstract Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

2020 ◽  
Author(s):  
Anja Katzenberger ◽  
Jacob Schewe ◽  
Julia Pongratz ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Models ◽  
Monsoon Rainfall ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Global Mean Temperature ◽  
Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

scholarly journals 21st century fate of the Mocho-Choshuenco ice cap in southern Chile

2020 ◽  
Author(s):  
Matthias Scheiter ◽  
Marius Schaefer ◽  
Eduardo Flández ◽  
Deniz Bozkurt ◽  
Ralf Greve
Keyword(s):  
21St Century ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Lake District ◽  
Equilibrium Line Altitude ◽  
Ice Dynamics ◽  
Ice Volume ◽  
Ice Cap ◽  
Global Circulation Models

Abstract. Glaciers and ice caps are thinning and retreating along the entire Andes ridge, and drivers of this mass loss vary between the different climate zones. The southern part of the Andes (Wet Andes) has the highest abundance of glaciers in number and size, and a proper understanding of ice dynamics is important to assess their evolution. In this contribution, we apply the ice sheet model SICOPOLIS to the Mocho-Choshuenco ice cap in the Chilean Lake District (40° S, 72° W, Wet Andes) to reproduce its current state and to project its evolution until the end of the 21st century under different global warming scenarios. First, we create a model spin-up using surface mass balance data observed on the south-eastern catchment, extrapolating them to the whole ice cap using an exposition-dependent parameterization. This spin-up is able to reproduce the most important present-day glacier features. Based on the spin-up, we then run the model 80 years into the future, forced by projected surface temperature anomalies from different global circulation models under different radiative pathway scenarios to obtain estimates of the ice cap's state by the end of the 21st century. The mean projected ice volume losses are 25 ± 19 % (RCP2.6), 64 ± 14 % (RCP4.5) and 94 ± 3 % (RCP8.5) with respect to the ice volume estimated by radio-echo sounding data from 2013. We estimate the uncertainty of our projections based on the spread of the results when forcing with different global climate models and on the uncertainty associated with the variation of the equilibrium line altitude with temperature change. Considering our results, we project an considerable deglaciation of the Chilean Lake District by the end of the 21st century.

A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change

2021 ◽  
Author(s):  
Thedini Asali Peiris ◽  
Petra Döll
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Net Radiation ◽  
Ensemble Mean ◽  
The Impact

<p>Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO<sub>2</sub> concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO<sub>2</sub> and climate. This active vegetation response consists of three components. With higher CO<sub>2</sub> concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.</p><p>Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.</p><p>The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.</p><p>We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).</p><p>Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.</p>

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