scholarly journals A bias-corrected CMIP6 global dataset for dynamical downscaling of future climate, 1979–2100

2021 ◽  
Author(s):  
Zhongfeng Xu ◽  
Ying Han ◽  
Chi-Yung Tam ◽  
Zong-Liang Yang ◽  
Congbin Fu
Keyword(s):  
Large Scale ◽  
Dynamical Downscaling ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Future Climate ◽  
Atmospheric Environment ◽  
Historical Period ◽  
Important Approach ◽  
Corrected Data ◽  
Mean Climate

Abstract Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. Here, we construct a set of bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a nonlinear trend from the mean of 18 CMIP6 models. The dataset spans the historical period of 1979–2014 and future scenarios (SSP245 and SSP585) of 2015–2100 with a horizontal resolution of 1.25° × 1.25° and 6-hourly intervals. Our evaluation suggests that the bias-corrected data shows clearly better quality than individual CMIP6 models evaluated in terms of climatological mean, interannual variance and extreme events. The presented dataset will be useful for the dynamical downscaling projections of future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

scholarly journals Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhongfeng Xu ◽  
Ying Han ◽  
Chi-Yung Tam ◽  
Zong-Liang Yang ◽  
Congbin Fu
Keyword(s):  
Large Scale ◽  
Dynamical Downscaling ◽  
Linear Trend ◽  
Coupled Model ◽  
Future Climate ◽  
Atmospheric Environment ◽  
Important Approach ◽  
Corrected Data ◽  
Mean Climate ◽  
The Individual

AbstractDynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

scholarly journals Historically-based run-time bias corrections substantially improve model projections of 100 years of future climate change

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Gerhard Krinner ◽  
Viatcheslav Kharin ◽  
Romain Roehrig ◽  
John Scinocca ◽  
Francis Codron
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Coupled Model ◽  
Atmospheric Model ◽  
Climate Impact ◽  
Future Climate ◽  
Future Climate Change ◽  
Historical Period ◽  
Underlying Assumption ◽  
Bias Corrections

Abstract Climate models and/or their output are usually bias-corrected for climate impact studies. The underlying assumption of these corrections is that climate biases are essentially stationary between historical and future climate states. Under very strong climate change, the validity of this assumption is uncertain, so the practical benefit of bias corrections remains an open question. Here, this issue is addressed in the context of bias correcting the climate models themselves. Employing the ARPEGE, LMDZ and CanAM4 atmospheric models, we undertook experiments in which one centre’s atmospheric model takes another centre’s coupled model as observations during the historical period, to define the bias correction, and as the reference under future projections of strong climate change, to evaluate its impact. This allows testing of the stationarity assumption directly from the historical through future periods for three different models. These experiments provide evidence for the validity of the new bias-corrected model approach. In particular, temperature, wind and pressure biases are reduced by 40–60% and, with few exceptions, more than 50% of the improvement obtained over the historical period is on average preserved after 100 years of strong climate change. Below 3 °C global average surface temperature increase, these corrections globally retain 80% of their benefit.

Some Pitfalls in Statistical Downscaling of Future Climate

2018 ◽  
Vol 99 (4) ◽  
pp. 791-803 ◽  
Author(s):  
John R. Lanzante ◽  
Keith W. Dixon ◽  
Mary Jo Nath ◽  
Carolyn E. Whitlock ◽  
Dennis Adams-Smith
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Added Value ◽  
Historical Period ◽  
Perfect Model

AbstractStatistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.

scholarly journals Tropical Atlantic Biases in CCSM4

2012 ◽  
Vol 25 (11) ◽  
pp. 3684-3701 ◽  
Author(s):  
Semyon A. Grodsky ◽  
James A. Carton ◽  
Sumant Nigam ◽  
Yuko M. Okumura
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Tropical Atlantic ◽  
Climate System Model ◽  
Stratocumulus Clouds ◽  
Subtropical Highs ◽  
The Tropics

This paper focuses on diagnosing biases in the seasonal climate of the tropical Atlantic in the twentieth-century simulation of the Community Climate System Model, version 4 (CCSM4). The biases appear in both atmospheric and oceanic components. Mean sea level pressure is erroneously high by a few millibars in the subtropical highs and erroneously low in the polar lows (similar to CCSM3). As a result, surface winds in the tropics are ~1 m s−1 too strong. Excess winds cause excess cooling and depressed SSTs north of the equator. However, south of the equator SST is erroneously high due to the presence of additional warming effects. The region of highest SST bias is close to southern Africa near the mean latitude of the Angola–Benguela Front (ABF). Comparison of CCSM4 to ocean simulations of various resolutions suggests that insufficient horizontal resolution leads to the insufficient northward transport of cool water along this coast and an erroneous southward stretching of the ABF. A similar problem arises in the coupled model if the atmospheric component produces alongshore winds that are too weak. Erroneously warm coastal SSTs spread westward through a combination of advection and positive air–sea feedback involving marine stratocumulus clouds. This study thus highlights three aspects to improve to reduce bias in coupled simulations of the tropical Atlantic: 1) large-scale atmospheric pressure fields; 2) the parameterization of stratocumulus clouds; and 3) the processes, including winds and ocean model resolution, that lead to errors in seasonal SST along southwestern Africa. Improvements of the latter require horizontal resolution much finer than the 1° currently used in many climate models.

scholarly journals HydroGFD3.0: a 25 km global near real-time updated precipitation and temperature data set

2020 ◽  
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Horizontal Resolution ◽  
Maximum Temperature ◽  
Historical Period ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Time Data ◽  
Data Set

Abstract. HydroGFD (Hydrological Global Forcing Data) is a data set of bias adjusted reanalysis data for daily precipitation, and minimum, mean, and maximum temperature. It is mainly intended for large scale hydrological modeling, but is also suitable for other impact modeling. The data set has an almost global land area coverage, excluding the Antarctic continent, at a horizontal resolution of 0.25°, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period; currently 1979 until five days ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology, and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number or wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly, such that no additional input data about the number of wet days is necessary. The daily mean temperature is adjusted toward the monthly mean of the observations, and applied to 1 h timesteps of the ERA5 reanalysis. Daily mean, minimum and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access to the data set at https://doi.org/10.5281/zenodo.3871707.

scholarly journals An Intercomparison of GCM and RCM Dynamical Downscaling for Characterizing the Hydroclimatology of California and Nevada

2018 ◽  
Vol 19 (9) ◽  
pp. 1485-1506 ◽  
Author(s):  
Zexuan Xu ◽  
Alan M. Rhoades ◽  
Hans Johansen ◽  
Paul A. Ullrich ◽  
William D. Collins
Keyword(s):  
Surface Temperature ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Dynamical Downscaling ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Total Precipitation ◽  
Microphysics Scheme ◽  
Reanalysis Dataset ◽  
Simulated Surface

Abstract Dynamical downscaling is a widely used technique to properly capture regional surface heterogeneities that shape the local hydroclimatology. However, in the context of dynamical downscaling, the impacts on simulation fidelity have not been comprehensively evaluated across many user-specified factors, including the refinements of model horizontal resolution, large-scale forcing datasets, and dynamical cores. Two global-to-regional downscaling methods are used to assess these: specifically, the variable-resolution Community Earth System Model (VR-CESM) and the Weather Research and Forecasting (WRF) Model with horizontal resolutions of 28, 14, and 7 km. The modeling strategies are assessed by comparing the VR-CESM and WRF simulations with consistent physical parameterizations and grid domains. Two groups of WRF Models are driven by either the NCEP reanalysis dataset (WRF_NCEP) or VR-CESM7 results (WRF_VRCESM) to evaluate the effects of large-scale forcing datasets. The simulated hydroclimatologies are compared with reference datasets for key properties including total precipitation, snow cover, snow water equivalent (SWE), and surface temperature. The large-scale forcing datasets are critical to the WRF simulations of total precipitation but not surface temperature, controlled by the wind field and atmospheric moisture transport at the ocean boundary. No significant benefit is found in the regional average simulated hydroclimatology by increasing horizontal resolution refinement from 28 to 7 km, probably due to the systematic biases from the diagnostic treatment of rainfall and snowfall in the microphysics scheme. The choice of dynamical core has little impact on total precipitation but significantly determines simulated surface temperature, which is affected by the snow-albedo feedback in winter and soil moisture estimations in summer.

scholarly journals Stability of ENSO and its tropical Pacific teleconnections over the Last Millennium

2015 ◽  
Vol 11 (10) ◽  
pp. 1347-1360 ◽  
Author(s):  
S. C. Lewis ◽  
A. N. LeGrande
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Historical Period ◽  
Last Millennium ◽  
Central Pacific ◽  
Proxy Records ◽  
The Pacific ◽  
The Stability

Abstract. Determining past changes in the amplitude, frequency and teleconnections of the El Niño-Southern Oscillation (ENSO) is important for understanding its potential sensitivity to future anthropogenic climate change. Palaeo-reconstructions from proxy records can provide long-term information of ENSO interactions with the background climatic state through time. However, it remains unclear how ENSO characteristics have changed on long timescales, and precisely which signals proxies record. Proxy interpretations are typically underpinned by the assumption of stationarity in relationships between local and remote climates, and often utilise archives from single locations located in the Pacific Ocean to reconstruct ENSO histories. Here, we investigate the long-term characteristics of ENSO and its teleconnections using the Last Millennium experiment of CMIP5 (Coupled Model Intercomparison Project phase 5; Taylor et al., 2012). We show that the relationship between ENSO conditions (NINO3.4) and local climates across the Pacific basin differs significantly for 100-year epochs defining the Last Millennium and the historical period 1906–2005. Furthermore, models demonstrate decadal- to centennial-scale modulation of ENSO behaviour during the Last Millennium. Overall, results suggest that the stability of teleconnections may be regionally dependent and that proxy climate records may reveal complex changes in teleconnected patterns, rather than large-scale changes in base ENSO characteristics. As such, proxy insights into ENSO may require evidence to be considered over large spatial areas in order to deconvolve changes occurring in the NINO3.4 region from those relating to local climatic variables. To obtain robust histories of the ENSO and its remote impacts, we recommend interpretations of proxy records should be considered in conjunction with palaeo-reconstructions from within the central Pacific.

scholarly journals Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Yuanyuan Ma ◽  
Yi Yang ◽  
Xiaoping Mai ◽  
Chongjian Qiu ◽  
Xiao Long ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Spectral Nudging ◽  
Environmental Prediction ◽  
Meteorological Elements

To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and spectral nudging) are debated. Moreover, dynamical downscaling is now performed at the convection-permitting scale to reduce the parameterization uncertainty and obtain the finer resolution. To compare the performances of the two nudging techniques in this study, three sensitivity experiments (with no nudging, analysis nudging, and spectral nudging) covering a period of two months with a grid spacing of 6 km over continental China are conducted to downscale the 1-degree National Centers for Environmental Prediction (NCEP) dataset with the Weather Research and Forecasting (WRF) model. Compared with observations, the results show that both of the nudging experiments decrease the bias of conventional meteorological elements near the surface and at different heights during the process of dynamical downscaling. However, spectral nudging outperforms analysis nudging for predicting precipitation, and analysis nudging outperforms spectral nudging for the simulation of air humidity and wind speed.

scholarly journals Projected Hydroclimate Changes on Hispaniola Island through the 21st Century in CMIP6 Models

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 6
Author(s):  
Dimitris A. Herrera ◽  
Rafael Mendez-Tejeda ◽  
Abel Centella-Artola ◽  
Daniel Martínez-Castro ◽  
Toby Ault ◽  
...  
Keyword(s):  
Soil Moisture ◽  
21St Century ◽  
Surface Soil ◽  
Dynamical Downscaling ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Cmip5 Models ◽  
Drought Risk ◽  
Surface Soil Moisture ◽  
Worst Case

Climate change might increase the frequency and severity of longer-lasting drought in the Caribbean, including in Hispaniola Island. Nevertheless, the hydroclimate changes projected by the state-of-the-art earth system models across the island remain unknown. Here, we assess 21st-century changes in hydroclimate over Hispaniola Island using precipitation, temperature, and surface soil moisture data from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). The resulting analysis indicates, as with the previous 5th Phase of CMIP (CMIP5) models, that Hispaniola Island might see a significant drying through the 21st century. The aridity appears to be robust in most of the island following the Shared Socioeconomic Pathways (SSP) 5–8.6, which assumes the “worst case” greenhouse gas emissions into the atmosphere. We find a significant reduction in both annual mean precipitation and surface soil moisture (soil’s upper 10 cm), although it appears to be more pronounced for precipitation (up to 26% and 11% for precipitation and surface soil moisture, respectively). Even though we provide insights into future hydroclimate changes on Hispaniola Island, CMIP6’s intrinsic uncertainties and native horizontal resolution precludes us to better assess these changes at local scales. As such, we consider future dynamical downscaling efforts that might help us to better inform policy-makers and stakeholders in terms of drought risk.

scholarly journals HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time

2021 ◽  
Vol 13 (4) ◽  
pp. 1531-1545
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Horizontal Resolution ◽  
Precipitation Anomaly ◽  
Maximum Temperature ◽  
Historical Period ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Time Data ◽  
Data Set

Abstract. HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25∘, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at https://doi.org/10.5281/zenodo.3871707 (Berg et al., 2020).

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