Predicting regional climate change: living with uncertainty

Regional climate prediction is not an insoluble problem, but it is a problem characterized by inherent uncertainty. There are two sources of this uncertainty: the unpredictability of the climatic and global systems. The climate system is rendered unpredictable by deterministic chaos; the global system renders climate prediction uncertain through the unpredictability of the external forcings imposed on the climate system. It is commonly inferred from the differences between climate models on regional scales that the models are deficient, but climate system unpredictability is such that this inference is premature; the differences are due to an unresolved combination of climate system unpredictability and model deficiencies. Since model deficiencies are discussed frequently and the two sources of inherent uncertainty are discussed only rarely, this review considers the implications of climatic and global system unpredictability for regional climate prediction. Consequently we regard regional climate prediction as a cascade of uncertainty, rather than as a single result process sullied by model deficiencies. We suggest three complementary methodological approaches: (1) the use of multiple forcing scenarios to cope with global system unpredictability; (2) the use of ensembles to cope with climate system unpredictability; and (3) the consideration of the entire response of the climate system to cope with the nature of climate change. We understand regional climate change in terms of changes in the general circulations of the atmosphere and oceans; so we illustrate the role of uncertainty in the task of regional climate prediction with the behaviour of the North Atlantic thermohaline circulation. In conclusion we discuss the implications of the uncertainties in regional climate prediction for research into the impacts of climate change, and we recognize the role of feedbacks in complicating the relatively simple cascade of uncertainties presented here.

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Selecting global climate models for regional climate change studies

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0900094106 ◽

2009 ◽

Vol 106 (21) ◽

pp. 8441-8446 ◽

Cited By ~ 353

Author(s):

D. W. Pierce ◽

T. P. Barnett ◽

B. D. Santer ◽

P. J. Gleckler

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Regional Climate ◽

Regional Climate Change ◽

Global Climate Models

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Estimating future changes in Alpine flash floods using CP-RCM projections

10.5194/egusphere-egu2020-9263 ◽

2020 ◽

Author(s):

Frederiek Sperna Weiland ◽

Pety Viguurs ◽

Marjanne Zander ◽

Albrecht Weerts

Keyword(s):

Climate Change ◽

High Resolution ◽

Climate Models ◽

Regional Climate ◽

Flash Floods ◽

Climate Prediction ◽

Alpine Region ◽

Parameter Estimates ◽

Projection System ◽

The Alps

Flash floods are a significant natural hazard in the Alpine region (FOEN, 2010). With changing rainfall regimes and decreased snow accumulation due to climate change, the risk of flash flood occurrence and timing thereof could change as well (Etchevers et al., 2002).In this study the frequency and occurrence of flash floods in the Alpine region is estimated for current and future climate (RCP8.5) using state-of-the-art high-resolution convection permitting climate models (CP-RCMs). For the historical period and far future (2100), data from an ensemble of convection permitting climate models (Ban et al., submitted 2019) was used to drive a high-resolution distributed hydrological model, i.e. the wflow_sbm model (Imhoff et al., 2019, Verseveld et al., 2020). The model domains cover the mountainous parts of the Danube, Rhone, Rhine and Po located in the Alps. &#160;The CP-RCM time-series available are of limited length due to computational constrains. At the same time the locations of flash floods vary per year therefore a regional scale analysis is made to assess whether in general the severity, frequency and timing of flash floods in the Alps will likely change under changing climate conditions.This research is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to support climate adaptation and mitigation decisions for the coming decades by developing a regional climate prediction and projection system based on high-resolution climate models for Europe.References:Etchevers, P., Golaz, C., Habets, F., and Noilhan, J., Impact of a climate change on the Rhone river catchment hydrology, J. Geophys. Res., 107( D16), doi:, 2002. Federal office for the environment FOEN (2010) Environment Switzerland 2011, Bern and Neuchatel 2011. Retrieved from www.environment-stat.admin.chImhoff, R.O., W. van Verseveld, B. van Osnabrugge, A.H. Weerts, 2019. Scaling point-scale pedotransfer functions parameter estimates for seamless large-domain high-resolution distributed hydrological modelling: An example for the Rhine river. Submitted to Water Resources Research, 2019.N. Ban, E. Brisson, C. Caillaud, E. Coppola, E. Pichelli, S. Sobolowski, &#8230;, M.J. Zander (submitted 2019): &#8220;The first multi-model ensemble of regional climate simulations at the kilometer-scale resolution, Part I: Evaluation of precipitation&#8221;, manuscript submitted for publication.

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High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3087-2018 ◽

2018 ◽

Vol 22 (5) ◽

pp. 3087-3103 ◽

Cited By ~ 12

Author(s):

Huanghe Gu ◽

Zhongbo Yu ◽

Chuanguo Yang ◽

Qin Ju ◽

Tao Yang ◽

...

Keyword(s):

Climate Change ◽

Tibetan Plateau ◽

Model Uncertainty ◽

Climate Models ◽

Southern China ◽

Regional Climate ◽

Regional Climate Change ◽

Internal Variability ◽

Northeastern China ◽

The Tibetan Plateau

Abstract. An ensemble simulation of five regional climate models (RCMs) from the coordinated regional downscaling experiment in East Asia is evaluated and used to project future regional climate change in China. The influences of model uncertainty and internal variability on projections are also identified. The RCMs simulate the historical (1980–2005) climate and future (2006–2049) climate projections under the Representative Concentration Pathway (RCP) RCP4.5 scenario. The simulations for five subregions in China, including northeastern China, northern China, southern China, northwestern China, and the Tibetan Plateau, are highlighted in this study. Results show that (1) RCMs can capture the climatology, annual cycle, and interannual variability of temperature and precipitation and that a multi-model ensemble (MME) outperforms that of an individual RCM. The added values for RCMs are confirmed by comparing the performance of RCMs and global climate models (GCMs) in reproducing annual and seasonal mean precipitation and temperature during the historical period. (2) For future (2030–2049) climate, the MME indicates consistent warming trends at around 1 ∘C in the entire domain and projects pronounced warming in northern and western China. The annual precipitation is likely to increase in most of the simulation region, except for the Tibetan Plateau. (3) Generally, the future projected change in annual and seasonal mean temperature by RCMs is nearly consistent with the results from the driving GCM. However, changes in annual and seasonal mean precipitation exhibit significant inter-RCM differences and possess a larger magnitude and variability than the driving GCM. Even opposite signals for projected changes in average precipitation between the MME and the driving GCM are shown over southern China, northeastern China, and the Tibetan Plateau. (4) The uncertainty in projected mean temperature mainly arises from the internal variability over northern and southern China and the model uncertainty over the other three subregions. For the projected mean precipitation, the dominant uncertainty source is the internal variability over most regions, except for the Tibetan Plateau, where the model uncertainty reaches up to 60 %. Moreover, the model uncertainty increases with prediction lead time across all subregions.

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Regional Climate Modeling over South America: A Review

Advances in Meteorology ◽

10.1155/2013/504357 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 43

Author(s):

Silvina A. Solman

Keyword(s):

Climate Change ◽

South America ◽

Climate Models ◽

Climate Modeling ◽

Regional Climate ◽

Regional Climate Change ◽

Regional Climate Models ◽

Regional Climate Modeling ◽

South American ◽

Sensitivity Studies

This review summarizes the progress achieved on regional climate modeling activities over South America since the early efforts at the beginning of the 2000s until now. During the last 10 years, simulations with regional climate models (RCMs) have been performed for several purposes over the region. Early efforts were mainly focused on sensitivity studies to both physical mechanisms and technical aspects of RCMs. The last developments were focused mainly on providing high-resolution information on regional climate change. This paper describes the most outstanding contributions from the isolated efforts to the ongoing coordinated RCM activities in the framework of the CORDEX initiative, which represents a major endeavor to produce ensemble climate change projections at regional scales and allows exploring the associated range of uncertainties. The remaining challenges in modeling South American climate features are also discussed.

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Proceedings of the training on regional climate change projections: Climate change analysis using CORDEX regional climate models over South Asia

10.53055/icimod.992 ◽

2021 ◽

Author(s):

Keyword(s):

Climate Change ◽

South Asia ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Change ◽

Regional Climate Models ◽

Change Analysis ◽

Climate Change Projections ◽

Climate Change Analysis

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Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-17-0127.1 ◽

2018 ◽

Vol 99 (10) ◽

pp. 2093-2106 ◽

Cited By ~ 6

Author(s):

Ambarish V. Karmalkar

Keyword(s):

Climate Change ◽

North America ◽

General Circulation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Change ◽

Internal Variability ◽

General Circulation Models ◽

Eastern United States ◽

The Impact

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

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The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6

Geoscientific Model Development ◽

10.5194/gmd-10-359-2017 ◽

2017 ◽

Vol 10 (1) ◽

pp. 359-384 ◽

Cited By ~ 86

Author(s):

Mark J. Webb ◽

Timothy Andrews ◽

Alejandro Bodas-Salcedo ◽

Sandrine Bony ◽

Christopher S. Bretherton ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Regional Climate ◽

Climate System ◽

Climate Feedback ◽

Grand Challenge ◽

Compact Set ◽

Coupled Models ◽

Cloud Feedbacks ◽

Non Linear

Abstract. The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions How does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?

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Impact analysis of climate change for an Alpine catchment using high resolution dynamic downscaling of ECHAM4 time slices

Hydrology and Earth System Sciences ◽

10.5194/hess-8-1031-2004 ◽

2004 ◽

Vol 8 (6) ◽

pp. 1031-1045 ◽

Cited By ~ 53

Author(s):

H. Kunstmann ◽

K. Schneider ◽

R. Forkel ◽

R. Knoche

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Regional Climate Change ◽

Climate Scenario ◽

Dynamic Downscaling ◽

Hydrological Simulation ◽

Recent Climate

Abstract. Global climate change affects spatial and temporal patterns of precipitation and so has a major impact on surface and subsurface water balances. While global climate models are designed to describe climate change on global or continental scales, their resolution is too coarse for them to be suitable for describing regional climate change. Therefore, regional climate models are applied to downscale the coarse meteorological fields to a much higher spatial resolution to take account of regional climate phenomena. The changes of atmospheric state due to regional climate change must be translated into surface and sub-surface water fluxes so that the impact on water balances in specific catchments can be investigated. This can be achieved by the coupled regional climatic/hydrological simulations presented here. The non-hydrostatic regional climate model MCCM was used for dynamic downscaling for two time slices of a global climate model simulation with the GCM ECHAM4 (IPCC scenario IS92a, "business as usual") from 2.8° × 2.8° to 4 × 4 km2 resolution for the years 1991–1999 and 2031–2039. This allowed derivation of detailed maps showing changes in precipitation and temperature in a region of southern Germany and the central Alps. The performance of the downscaled ECHAM4 to reproduce the seasonality of precipitation in central Europe for the recent climate was investigated by comparison with dynamically downscaled ECMWF reanalyses in 20 × 20 km2 resolution. The downscaled ECHAM4 fields underestimate precipitation significantly in summer. The ratio of mean monthly downscaled ECHAM4 and ECMWF precipitation showed little variation, so it was used to adjust the course of precipitation for the ECHAM4/MCCM fields before it was applied in the hydrological model. The high resolution meteorological fields were aggregated to 8-hour time steps and applied to the distributed hydrological model WaSiM to simulate the water balance of the alpine catchment of the river Ammer (c. 700 km2) at 100 × 100 m2 resolution. To check the reliability of the coupled regional climatic/hydrological simulation results for the recent climate, they were compared with those of a station-based hydrological simulation for the period 1991–1999. This study shows the changes in the temperature and precipitation distributions in the catchment from the recent climate to the future climate scenario and how these will affect the frequency distribution of runoff. Keywords: coupled climate-hydrology simulations, dynamic downscaling, distributed hydrological modelling, ECHAM4 climate scenario, alpine hydrology

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