Climate change in the High Mountain Asia simulated with CMIP6 models

2021 ◽  
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Climate Models ◽  
Global Climate Models ◽  
Cold Bias ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Model Biases ◽  
Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 °C (± 3.2 °C), associated with an over-extended relative snow cover extent of 53 % (± 62 %), and a relative excess of precipitation of 139 % (± 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 °C (± 0.5 °C), 3.4 °C (± 0.7 °C), 5.2 °C (± 1.2 °C), and 6.6 °C (± 1.5 °C); a relative decrease in the snow cover extent of 10 % (± 4.1 %), 19 % (± 5 %), 29 % (± 8 %), and 35 % (± 9 %); and an increase in total precipitation of 9 % (± 5 %), 13 % (± 7 %), 19 % (± 11 %), and 27 % (± 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

Extending the paleoglaciological record of the southeastern Tibetan Plateau by combining geochronological and high-resolution remote sensing techniques

2020 ◽  
Author(s):  
Arjen P. Stroeven ◽  
Ramona A.A. Schneider ◽  
Robin Blomdin ◽  
Natacha Gribenski ◽  
Marc W. Caffee ◽  
...  
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Luminescence Dating ◽  
The Tibetan Plateau ◽  
Exposure Dating ◽  
Southeastern Margin ◽  
Downstream Communities

<p>Paleoglaciological data is a crucial source of information towards insightful paleoclimate reconstructions by providing vital boundary conditions for regional and global climate models. In this context, the Third Pole Environment is considered a key region because it is highly sensitive to global climate change and its many glaciers constitute a diminishing but critical supply of freshwater to downstream communities in SE Asia. Despite its importance, extents of past glaciation on the Tibetan Plateau remain poorly documented or controversial largely because of the lack of well define glacial chronostratigraphies and reconstructions of former glacier extent. This study contributes to a better documentation of the extent and improved resolution of the timing of past glaciations on the southeastern margin of the Tibetan Plateau. We deploy a high-resolution TanDEM-X Digital Elevation Model (12 m resolution) to produce maps of glacial and proglacial fluvial landforms in unprecedented detail. Geomorphological and sedimentological field observations complement the mapping while cosmogenic nuclide exposure dating of quartz samples from boulders on end moraines detail the timing of local glacier expansion. Additionally, samples for optically stimulated luminescence dating were taken from extensive and distinct terraces located in pull-apart basins downstream of the end moraines to determine their formation time. We compare this new dataset with new and published electron spin resonance ages from terraces. Temporal coherence between the different chronometers strengthens the geochronological record while divergence highlights limitations in the applicability of the chronometers to glacial research or in our conceptual understanding of landscape changes in tectonic regions. Results highlight our current understanding of paleoglaciation, landscape development, and paleoclimate on the SE Tibetan Plateau.</p>

scholarly journals Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs

2020 ◽  
Vol 59 (2) ◽  
pp. 207-235 ◽  
Author(s):  
Lei Zhang ◽  
YinLong Xu ◽  
ChunChun Meng ◽  
XinHua Li ◽  
Huan Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
The Tibetan Plateau ◽  
Dynamic Downscaling

AbstractIn aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

scholarly journals Climate change in the High Mountain Asia in CMIP6

2021 ◽  
Vol 12 (4) ◽  
pp. 1061-1098
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner ◽  
Kathrin Naegeli ◽  
Stefan Wunderle
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Relative Bias ◽  
The Other ◽  
Surface Elevation ◽  
High Mountain ◽  
Model Biases ◽  
Snow Cover Extent

Abstract. Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of general circulation models (GCMs) shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] ∘C (90 % confidence interval) in comparison with the Climate Research Unit (CRU) observational dataset, associated with a snow cover mean overestimation of 12 % [−13 % to 43 %], corresponding to a relative bias of 52 % [−53 % to 183 %] in comparison with the NOAA Climate Data Record (CDR) satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm d−1, corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models with the best performance for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends on the model biases is found for some variables and seasons; however, some highly biased models fall within the range of observed trends, suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] ∘C for SSP1-2.6 to 6.5 [4.9 to 9.0] ∘C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 % [−16.4 % to −5.0 %] to −32.2 % [−49.1 % to −25.0 %] and a relative median precipitation increase from 8.5 % [4.8 % to 18.2 %] to 24.9 % [14.4 % to 48.1 %] by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the global surface air temperature (GSAT), except for summer snow cover which shows a slower decrease at strong levels of GSAT.

scholarly journals Climate change in the High Mountain Asia in CMIP6

2021 ◽  
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner ◽  
Kathrin Naegeli ◽  
Stefan Wunderle
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Relative Bias ◽  
The Other ◽  
Surface Elevation ◽  
High Mountain ◽  
Model Biases ◽  
Snow Cover Extent

Abstract. Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four shared socioeconomic pathways SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 CMIP6 models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of GCMs shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] °C (90 % confidence interval) in comparison with the CRU observational dataset, associated with a snow cover mean overestimation of 12 [−13 to 43] %, corresponding to a relative bias of 52 [−53 to 183] % in comparison with the NOAA CDR satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm day−1, corresponding to a relative bias of 143 [31 to 281] %, but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another one. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models performing the best for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends to the model biases is found for some variables and seasons, however, some highly biased models fall within the range of observed trends suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] °C for SSP1-2.6 to 6.5 [4.9 to 9.0] °C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 [−16.4 to −5.0] % to −32.2 [−49.1 to −25.0] % and a relative median precipitation increase from 8.5 [4.8 to 18.2] % to 24.9 [14.4 to 48.1] % by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the Global Surface Air Temperature (GSAT), except for summer snow cover that shows a slower decrease at strong levels of GSAT.

scholarly journals Quantifying the added value of high resolution climate models: A systematic comparison of WRF simulations for complex Himalayan terrain

2017 ◽  
Author(s):  
Ramchandra Karki ◽  
Shabeh Hasson ◽  
Lars Gerlitz ◽  
Udo Schickhoff ◽  
Thomas Scholten ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Regional Scale ◽  
Wrf Model ◽  
Global Climate Models ◽  
Added Value ◽  
Cold Bias ◽  
Near Surface ◽  
Monsoonal Precipitation ◽  
High Elevations

Abstract. Mesoscale dynamical refinements of global climate models or atmospheric reanalysis have shown their potential to resolve the intricate atmospheric processes, their land surface interactions, and subsequently, realistic distribution of climatic fields in complex terrains. Given that such potential is yet to be explored within the central Himalayan region of Nepal, we investigate the skill of the Weather Research and Forecasting (WRF) model with different spatial resolutions in reproducing the spatial, seasonal and diurnal characteristics of the near-surface air temperature and precipitation, as well as, the spatial shifts in the diurnal monsoonal precipitation peak over the Khumbu (Everest), Rolwaling and adjacent southern areas. Therefore, the ERA-Interim (0.75°) reanalysis has been dynamically refined to 25, 5 and 1 km (D1, D2 and D3) for one complete hydrological year (Oct 2014–Sep 2015), using the one-way nested WRF model run with mild-nudging and parameterized convection for the outer but explicitly resolved convection for the inner domains. Our results suggest that D3 realistically reproduces the monsoonal precipitation, as compared to its underestimation by D1 but overestimation by D2. All three resolutions however overestimate precipitation from the westerly disturbances, owing to simulating anomalously higher intensity of few intermittent events. Temperatures are though generally well reproduced by all resolutions, winter and pre-monsoon seasons feature a high cold bias for high elevations while lower show a simultaneous warm bias. Contrary to higher resolutions, D1 fails to realistically reproduce the regional-scale nocturnal monsoonal peak precipitation observed at the Himalayan foothills and its diurnal shift towards high elevations, whereas D2 resolves these characteristics but exhibits a limited skill in reproducing such peak at the river valley scale due to the limited representation of the narrow valleys at 5 km resolution. Nonetheless, featuring a substantial skill over D1 and D2, D3 simulates almost realistic shapes of the seasonal and diurnal precipitation and the peak timings even at valley scales. These findings clearly suggest an added value of the convective scale resolutions in realistically resolving the topo-climates over the central Himalaya, which in turn allow simulating their interactions with the synoptic scale weather systems prevailing over High Asia.

Extreme Events in High Resolution CMCC Regional and Global Climate Models

2011 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Antonella Sanna ◽  
Edoardo Bucchignani ◽  
Myriam Montesarchio
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

scholarly journals Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

2019 ◽  
Vol 32 (19) ◽  
pp. 6467-6490 ◽  
Author(s):  
Kimmo Ruosteenoja ◽  
Timo Vihma ◽  
Ari Venäläinen
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
Seasonal Wind ◽  
Projected Changes

Abstract Future changes in geostrophic winds over Europe and the North Atlantic region were studied utilizing output data from 21 CMIP5 global climate models (GCMs). Changes in temporal means, extremes, and the joint distribution of speed and direction were considered. In concordance with previous research, the time mean and extreme scalar wind speeds do not change pronouncedly in response to the projected climate change; some degree of weakening occurs in the majority of the domain. Nevertheless, substantial changes in high wind speeds are identified when studying the geostrophic winds from different directions separately. In particular, in northern Europe in autumn and in parts of northwestern Europe in winter, the frequency of strong westerly winds is projected to increase by up to 50%. Concurrently, easterly winds become less common. In addition, we evaluated the potential of the GCMs to simulate changes in the near-surface true wind speeds. In ocean areas, changes in the true and geostrophic winds are mainly consistent and the emerging differences can be explained (e.g., by the retreat of Arctic sea ice). Conversely, in several GCMs the continental wind speed response proved to be predominantly determined by fairly arbitrary changes in the surface properties rather than by changes in the atmospheric circulation. Accordingly, true wind projections derived directly from the model output should be treated with caution since they do not necessarily reflect the actual atmospheric response to global warming.

scholarly journals Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

2013 ◽  
Vol 17 (10) ◽  
pp. 3921-3936 ◽  
Author(s):  
M. Ménégoz ◽  
H. Gallée ◽  
H. W. Jacobi
Keyword(s):  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
The Tibetan Plateau ◽  
Monsoon Period ◽  
Medium Range Weather Forecast ◽  
Snow Cover Extent ◽  
The Himalaya

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF) used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

scholarly journals An evaluation of the importance of spatial resolution in a global climate and hydrological model based on the Rhine and Mississippi basin

2017 ◽  
Author(s):  
Imme Benedict ◽  
Chiel C. van Heerwaarden ◽  
Albrecht H. Weerts ◽  
Wilco Hazeleger
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Scale ◽  
Global Climate Models ◽  
Convective Processes ◽  
Parameter Values

Abstract. The hydrological cycle of river basins can be simulated by combining global climate models (GCMs) and global hydrological models (GHMs). The spatial resolution of these models is restricted by computational resources and therefore limits the processes and level of detail that can be resolved. To further improve simulations of precipitation and river-runoff on a global scale, we assess and compare the benefits of an increased resolution for a GCM and a GHM. We focus on the Rhine and Mississippi basin. Increasing the resolution of a GCM (1.125° to 0.25°) results in more realistic large-scale circulation patterns over the Rhine and an improved precipitation budget. These improvements with increased resolution are not found for the Mississippi basin, most likely because precipitation is strongly dependent on the representation of still unresolved convective processes. Increasing the resolution of vegetation and orography in the high resolution GHM (from 0.5° to 0.05°) shows no significant differences in discharge for both basins, because the hydrological processes depend highly on other parameter values that are not readily available at high resolution. Therefore, increasing the resolution of the GCM provides the most straightforward route to better results. This route works best for basins driven by large-scale precipitation, such as the Rhine basin. For basins driven by convective processes, such as the Mississippi basin, improvements are expected with even higher resolution convection permitting models.

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