Multiscale Simulation of Precipitation over East Asia by Variable Resolution CAM-MPAS

<p>Traditional global climate models (GCMs) with coarse uniform resolution (UR) usually have deficiency in simulating realistic results at regional scale, while experimental global high-resolution models show benefits but also raise much computational burden. In recent years, variable resolution (VR) models with unstructured mesh are found to provide comparable results at regional scale and require less computational resources. In this study, the variable resolution CAM-MPAS model with the MPAS (Model for Prediction Across Scales) dynamical core coupled with CAM5 (Community Atmosphere Model Version 5) physics package is used to evaluate the effect of 30 km regional refinement over East Asia on the precipitation simulation. Our results show that the CAM-MPAS model can reasonably reproduce the annual and seasonal precipitation over East Asia, and the MPAS-VR simulation shows reduced mean bias and improvements in seasonal cycle, intensity distribution, and interannual variation compared with the low resolution MPAS-UR simulation. Furthermore, the major contribution to the improvements over the Tibet Plateau in the MPAS-VR experiment comes from the increase of the grid spacing rather than the terrain resolution.</p>

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Cold season emissions dominate the Arctic tundra methane budget

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1516017113 ◽

2015 ◽

Vol 113 (1) ◽

pp. 40-45 ◽

Cited By ~ 149

Author(s):

Donatella Zona ◽

Beniamino Gioli ◽

Róisín Commane ◽

Jakob Lindaas ◽

Steven C. Wofsy ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Terrestrial Ecosystems ◽

Arctic Tundra ◽

Cold Season ◽

Global Climate Models ◽

The Arctic ◽

Late Season ◽

Aircraft Data

Arctic terrestrial ecosystems are major global sources of methane (CH4); hence, it is important to understand the seasonal and climatic controls on CH4 emissions from these systems. Here, we report year-round CH4 emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥50% of the annual CH4 flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the “zero curtain” period, when subsurface soil temperatures are poised near 0 °C. The zero curtain may persist longer than the growing season, and CH4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH4 derived from aircraft data demonstrate the large spatial extent of late season CH4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH4 y−1, ∼25% of global emissions from extratropical wetlands, or ∼6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.

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Observing regional-scale pack-ice decay processes with helicopter-borne sensors and moored upward-looking sonars

Annals of Glaciology ◽

10.3189/172756411795931688 ◽

2011 ◽

Vol 52 (57) ◽

pp. 35-42 ◽

Cited By ~ 13

Author(s):

Simon J. Prinsenberg ◽

Ingrid K. Peterson

Keyword(s):

Climate Models ◽

Global Climate ◽

Spatial Scales ◽

Regional Scale ◽

Global Climate Models ◽

First Year ◽

Coarse Scale ◽

Interaction Processes ◽

Pack Ice ◽

Ice Floes

AbstractThe variability of Arctic pack-ice parameters (e.g. extent and ice type) has been monitored by satellite-borne sensors since the early 1960s, and information on ice thickness is now becoming available from satellite altimeters. However, the spatial resolution of satellite-derived ice properties is too coarse to validate fine-scale ice variability generated by regional-scale interaction processes that affect the coarse-scale pack-ice albedo, strength and decay. To understand these regional processes, researchers rely on other data-monitoring platforms such as moored upward-looking sonars and helicopter-borne sensors. Backed by observations, two such regional-scale pack-ice decay processes are discussed: the break-up of large pack-ice floes by long-period waves generated by distant storms, and the spring decay of first-year-ice ridges in a diverging pack-ice environment. These two processes, although occurring on regional spatial scales, are important contributors to the evolution of the total pack ice and need to be included in global climate models, especially as the conditions for their occurrence will alter due to climate change.

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Statistical Downscaling for Climate Science

Oxford Research Encyclopedia of Climate Science ◽

10.1093/acrefore/9780190228620.013.712 ◽

2019 ◽

Cited By ~ 1

Author(s):

Douglas Maraun

Keyword(s):

Statistical Downscaling ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Global Climate Model ◽

Regional Scale ◽

Climatic Changes ◽

Global Climate Models ◽

Small Scale ◽

Climate Projections

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

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Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Atmosphere ◽

10.3390/atmos11111245 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1245

Author(s):

Frank Kreienkamp ◽

Philip Lorenz ◽

Tobias Geiger

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Coupled Model ◽

Global Climate Models ◽

Climate Projections ◽

Climate Modelling ◽

Climate Signal ◽

Temperature Signal

Climate modelling output that was provided under the latest Coupled Model Intercomparison Project (CMIP6) shows significant changes in model-specific Equilibrium Climate Sensitivity (ECS) as compared to CMIP5. The newer versions of many Global Climate Models (GCMs) report higher ECS values that result in stronger global warming than previously estimated. At the same time, the multi-GCM spread of ECS is significantly larger than under CMIP5. Here, we analyse how the differences between CMIP5 and CMIP6 affect climate projections for Germany. We use the statistical-empirical downscaling method EPISODES in order to downscale GCM data for the scenario pairs RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. We use data sets of the GCMs CanESM, EC-Earth, MPI-ESM, and NorESM. The results show that the GCM-specific changes in the ECS also have an impact at the regional scale. While the temperature signal under regional climate change remains comparable for both CMIP generations in the MPI-ESM chain, the temperature signal increases by up to 3 °C for the RCP8.5/SSP5-8.5 scenario pair in the EC-Earth chain. Changes in precipitation are less pronounced and they only show notable differences at the seasonal scale. The reported changes in the climate signal will have direct consequences for society. Climate change impacts previously projected for the high-emission RCP8.5 scenario might occur equally under the new SSP2-4.5 scenario.

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Considerations in the Selection of Global Climate Models for Regional Climate Projections: The Arctic as a Case Study*

Journal of Climate ◽

10.1175/2010jcli3462.1 ◽

2011 ◽

Vol 24 (6) ◽

pp. 1583-1597 ◽

Cited By ~ 65

Author(s):

James E. Overland ◽

Muyin Wang ◽

Nicholas A. Bond ◽

John E. Walsh ◽

Vladimir M. Kattsov ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Model Performance ◽

Global Climate Models ◽

The Arctic ◽

Climate Projections ◽

Scale Evaluation ◽

Continental Scale ◽

Societal Relevance

Abstract Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere–ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics—a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an “ensemble of opportunity,” the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

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Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models

Earth System Dynamics Discussions ◽

10.5194/esdd-6-1-2015 ◽

2015 ◽

Vol 6 (1) ◽

pp. 1-30 ◽

Cited By ~ 3

Author(s):

I. Giuntoli ◽

J.-P. Vidal ◽

C. Prudhomme ◽

D. M. Hannah

Keyword(s):

Climate Models ◽

Hydrological Cycle ◽

Global Climate ◽

Regional Scale ◽

Global Climate Models ◽

Hydrological Models ◽

Uncertainty Range ◽

Low Flows ◽

Hydrological Extremes ◽

High Flows

Abstract. Projections of changes in the hydrological cycle from Global Hydrological Models (GHMs) driven by Global Climate Models (GCMs) are critical for understanding future occurrence of hydrological extremes. However, uncertainties remain large and need to be better assessed. In particular, recent studies have pointed to a considerable contribution of GHMs that can equal or outweigh the contribution of GCMs to uncertainty in hydrological projections. Using 6 GHMs and 5 GCMs from the ISI-MIP multi-model ensemble, this study aims: (i) to assess future changes in the frequency of both high and low flows at the global scale using control and future (RCP8.5) simulations by the 2080s, and (ii) to quantify, for both ends of the runoff spectrum, GCMs and GHMs contributions to uncertainty using a 2-way ANOVA. Increases are found in high flows for northern latitudes and in low flows for several hotspots. Globally, the largest source of uncertainty is associated with GCMs, but GHMs are the greatest source in snow dominated regions. More specifically, results vary depending on the runoff metric, the temporal (annual and seasonal) and regional scale of analysis. For instance, uncertainty contribution from GHMs is higher for low flows than it is for high flows, partly owing to the different processes driving the onset of the two phenomena (e.g. the more direct effect of the GCMs precipitation variability on high flows). This study provides a comprehensive synthesis of where future hydrological extremes are projected to increase and where the ensemble spread is owed to either GCMs or GHMs. Finally, our results underline the importance of using multiple GCMs and GHMs to envelope the overall uncertainty range and the need for improvements in modeling snowmelt and runoff processes to project future hydrological extremes.

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Ability of global climate models at a regional scale over Australia

International Journal of Global Environmental Issues ◽

10.1504/ijgenvi.2007.016108 ◽

2007 ◽

Vol 7 (4) ◽

pp. 275 ◽

Cited By ~ 2

Author(s):

Sarah E. Perkins ◽

Andy J. Pitman ◽

Neil J. Holbrook ◽

John McAneney

Keyword(s):

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Global Climate Models

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Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

Water ◽

10.3390/w10111516 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1516 ◽

Cited By ~ 2

Author(s):

Zhijie Ta ◽

Yang Yu ◽

Lingxiao Sun ◽

Xi Chen ◽

Guijin Mu ◽

...

Keyword(s):

Central Asia ◽

Climate Models ◽

Global Climate ◽

Regional Climate ◽

Coupled Model ◽

Regional Climate Change ◽

Research Unit ◽

Global Climate Models ◽

Climatic Research Unit ◽

Precipitation Simulation

The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.

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Regional‐scale surface hydrologic simulations from global climate models: A case study

ATMOSPHERE-OCEAN ◽

10.1080/07055900.1991.9649411 ◽

1991 ◽

Vol 29 (3) ◽

pp. 420-436 ◽

Cited By ~ 1

Author(s):

G. Thomas ◽

A. Henderson‐Sellers ◽

A.J. Pitman

Keyword(s):

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Global Climate Models ◽

Scale Surface

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A simple tool for refining GCM water availability projections, applied to Chinese catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-22-6043-2018 ◽

2018 ◽

Vol 22 (11) ◽

pp. 6043-6057

Author(s):

Joe M. Osborne ◽

F. Hugo Lambert

Keyword(s):

21St Century ◽

River Runoff ◽

Water Availability ◽

Climate Models ◽

Yellow River ◽

Human Impacts ◽

Global Climate ◽

Regional Scale ◽

Global Climate Models

Abstract. There is a growing desire for reliable 21st-century projections of water availability at the regional scale. Global climate models (GCMs) are typically used together with global hydrological models (GHMs) to generate such projections. GCMs alone are unsuitable, especially if they have biased representations of aridity. The Budyko framework represents how water availability varies as a non-linear function of aridity and is used here to constrain projections of runoff from GCMs, without the need for computationally expensive GHMs. Considering a Chinese case study, we first apply the framework to observations to show that the contribution of direct human impacts (water consumption) to the significant decline in Yellow River runoff was greater than the contribution of aridity change by a factor of approximately 2, although we are unable to rule out a significant contribution from the net effect of all other factors. We then show that the Budyko framework can be used to narrow the range of Yellow River runoff projections by 34 %, using a multi-model ensemble and the high-end Representative Concentration Pathway (RCP8.5) emissions scenario. This increases confidence that the Yellow River will see an increase in runoff due to aridity change by the end of the 21st century. Yangtze River runoff projections change little, since aridity biases in GCMs are less substantial. Our approach serves as a quick and inexpensive tool to rapidly update and correct projections from GCMs alone. This could serve as a valuable resource when determining the water management policies required to alleviate water stress for future generations.

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