Dataset Information

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

ABSTRACT: A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis ( https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317 ) for the historical (1981-2014) and future (2015-2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.

SUBMITTER: Gebrechorkos S

PROVIDER: S-EPMC10495318 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

Gebrechorkos Solomon S Leyland Julian J Slater Louise L Wortmann Michel M Ashworth Philip J PJ Bennett Georgina L GL Boothroyd Richard R Cloke Hannah H Delorme Pauline P Griffith Helen H Hardy Richard R Hawker Laurence L McLelland Stuart S Neal Jeffrey J Nicholas Andrew A Tatem Andrew J AJ Vahidi Ellie E Parsons Daniel R DR Darby Stephen E SE

Scientific data 20230911 1

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and mi ...[more]

PMID: 37696836

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Project description:Climate change impact studies need climate projections for different scenarios and at scales relevant to planning and management, preferably for a variety of models and realizations to capture the uncertainty in these models. To address current gaps, we statistically downscaled (SD) 3-7 CMIP6 models for five key indicators of marine habitat conditions: temperature, salinity, pH, oxygen, and chlorophyll across European waters for three climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results provide ensemble averages and uncertainty estimates that can serve as input data for projecting the potential success of a range of Nature-based Solutions, including the restoration of habitat-forming species such as seagrass in the Mediterranean and kelp in coastal areas of Portugal and Norway. Evaluation of the ensemble with observations from four European regions (North Sea, Baltic Sea, Bay of Biscay, and Mediterranean Sea) indicates that the SD projections realistically capture the climatological conditions of the historical period 1993-2020. Model skill (Liu-mean efficiency, Pearson correlation) clearly improves for both surface temperature and oxygen across all regions with respect to the original ESMs demonstrating a higher skill for temperature compared to oxygen. Warming is evident across all areas and large differences among scenarios fully emerge from the background uncertainties related to internal variability and model differences in the second half of the century. Scenario-specific differences in acidification significantly emerge from model uncertainty and internal variability leading to distinct trajectories in surface pH starting before mid-century (in some cases starting from present day). Deoxygenation is also present across all domains, but the climate signal was significantly weaker compared to the other two indicators when compared to model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is less distinct. The substantial regional and local heterogeneity in these three abiotic indicators underscores the need for highly spatially resolved physical and biogeochemical projections to understand how climate change may impact marine ecosystems.

Project description:BACKGROUND:Compilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed. RESULTS:This study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2 emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (< 10% difference), and reach good consistency in half of the cities examined (< 30% difference). The ODIAC dataset exhibits a much higher emission compared to inventory estimates in Cape Town (+ 148%), Sao Paulo (+ 43%) and Beijing (+ 40%), possibly related to poor correlation between nightlight intensity with human activity, such as the high-emission and low-lighting industrial parks in developing countries. On the other hand, ODIAC shows lower estimates in Manhattan (- 62%), New York City (- 45%), Washington D.C. (- 42%) and Toronto (- 33%), all located in North America, which may be attributable to an underestimation of residential emissions from heating in ODIAC's nightlight-based approach, and an overestimation of emission from ground transportation in registered vehicles statistics of inventory estimates. CONCLUSIONS:The relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2 emission, which is valuable for atmosphere CO2 inversion modeling and comparing with satellite CO2 observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future.

Dataset Information

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

Publications

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

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