scholarly journals Update to the Global Climate Data package: analysis of empirical bias correction methods in the context of producing very high resolution climate projections

2017 ◽  
Vol 38 (2) ◽  
pp. 825-840 ◽  
Author(s):  
Thomas M. Mosier ◽  
David F. Hill ◽  
Kendra V. Sharp
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Global Climate ◽  
Climate Projections ◽  
Climate Data ◽  
Very High

Copernicus Sectoral Information System for the Biodiversity Sector

2020 ◽  
Author(s):  
Koen De Ridder ◽  
Filip Lefebre ◽  
Eline Vanuytrecht ◽  
Julie Berckmans ◽  
Hendrik Wouters
Keyword(s):  
Climate Change ◽  
Information System ◽  
High Resolution ◽  
Global Climate ◽  
Future Climate ◽  
Use Cases ◽  
Climate Projections ◽  
Climate Data ◽  
Climate Conditions ◽  
Climate Suitability

<p>Biodiversity is increasingly under pressure from climate change, which affects the habitat suitability for species as well as the efficiency of ecosystem services. Management of these issues, for instance through ecosystem restoration or species dispersal measures, is often hindered by a lack of appropriate information about (future) climate conditions.  To address this, an operational Sectoral Information System (SIS) for the Biodiversity sector (SIS Biodiversity) is designed within the Copernicus programme Climate Change Service (C3S). This new SIS provides tailored bio-climatic indicators and applications, and delivers novel evidence regarding impacts of past, present and future climate. As such, it provides support to decision making challenges that are currently facing unmet climate data needs.<br> <br>The new climate service for SIS Biodiversity will be demonstrated, including the outline, workflow and outcomes of the use cases. The service is built upon the Copernicus Data Store platform (CDS; ), and takes into account (1) the barriers in ongoing bio-climate assessments and (2) the user requirements of diverse stakeholders (e.g. researcher institutes, local NGO’s, the International Union for Conservation of Nature and Natural Resources (IUCN),…). These have been collected during workshops and bilateral meetings in 2019. A common barrier is the lack of reliable and high-resolution information about states and dynamics of the soil, sea, ice and air for the past and the future climate. Therefore, the service provides relevant bio-climatic indicators on the basis of a wealth of available variables from the latest ERA5 reanalysis datasets and the CMIP5 global climate projections available in CDS. In order to provide information at high resolution and minimize inconsistencies between observed and modelled variables, different downscaling and bias-correction techniques are applied. A common requirement is a universal and flexible interface to the bio-climatic indicators in an easy-to-use and coherent platform that is applicable for different fauna and flora species of interest. Therefore, different applications have been developed within CDS for generating bio-climate suitability envelopes from the high-resolution indicators and to evaluate climate suitability and impacts for the species under present and future climate. Finally, the service is currently tested and refined on the basis of specific use cases. Special attention is given to their transferability to other global and topical studies, hence maximizing external user uptake throughout existing research and policy networks.</p>

scholarly journals High-Resolution Climate Projections for a Densely Populated Mediterranean Region

2020 ◽  
Vol 12 (9) ◽  
pp. 3684
Author(s):  
Mohamed Salem Nashwan ◽  
Shamsuddin Shahid ◽  
Eun-Sung Chung
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Climate Projections ◽  
Near Future ◽  
Far Future

The present study projected future climate change for the densely populated Central North region of Egypt (CNE) for two representative concentration pathways (RCPs) and two futures (near future: 2020–2059, and far future: 2060–2099), estimated by a credible subset of five global climate models (GCMs). Different bias correction models have been applied to correct the bias in the five interpolated GCMs’ outputs onto a high-resolution horizontal grid. The 0.05° CNE datasets of maximum and minimum temperatures (Tmx, and Tmn, respectively) and the 0.1° African Rainfall Climatology (ARC2) datasets represented the historical climate. The evaluation of bias correction methodologies revealed the better performance of linear and variance scaling for correcting the rainfall and temperature GCMs’ outputs, respectively. They were used to transfer the correction factor to the projections. The five statistically bias-corrected climate projections presented the uncertainty range in the future change in the climate of CNE. The rainfall is expected to increase in the near future but drastically decrease in the far future. The Tmx and Tmn are projected to increase in both future periods reaching nearly a maximum of 5.50 and 8.50 °C for Tmx and Tmn, respectively. These findings highlighted the severe consequence of climate change on the socio-economic activities in the CNE aiming for better sustainable development.

scholarly journals Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

2020 ◽  
Vol 12 (2) ◽  
pp. 305 ◽  
Author(s):  
Tom Akkermans ◽  
Nicolas Clerbaux
Keyword(s):  
High Resolution ◽  
Global Climate ◽  
Regional Scale ◽  
Radiant Energy ◽  
Large Data ◽  
Energy System ◽  
Climate Data ◽  
Data Set ◽  
Regional Analyses ◽  
Very High

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

scholarly journals New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

2019 ◽  
Vol 58 (12) ◽  
pp. 2617-2632 ◽  
Author(s):  
Qifen Yuan ◽  
Thordis L. Thorarinsdottir ◽  
Stein Beldring ◽  
Wai Kwok Wong ◽  
Shaochun Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Data Product ◽  
Temporal Features ◽  
Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

scholarly journals A 40-year global dataset of visible channel remote sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Author(s):  
Benjamin R. Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Climate Data Record ◽  
Very High

Abstract. A consistently calibrated 40-year length dataset of visible channel remote sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time-series. The dataset uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record (CDR) for top-of-atmosphere (TOA) visible channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote sensing reflectance. The resulting dataset is produced at daily, and archived at monthly, resolution, on a 0.1° × 0.1° grid at https://doi.pangaea.de/10.1594/PANGAEA.892175. The primary aim of deriving this dataset is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania Huxleyi over the past 40 years.

Short-duration rainfall extremes in very high-resolution climate projections: historical evaluation and future projections

2020 ◽  
Author(s):  
Jonas Olsson ◽  
Johanna Sörensen ◽  
Yiheng Du ◽  
Dong An ◽  
Peter Berg ◽  
...  
Keyword(s):  
High Resolution ◽  
Short Duration ◽  
Climate Model ◽  
Climate Adaptation ◽  
Climate Projections ◽  
Climate Factors ◽  
Hourly Rainfall ◽  
Rainfall Extremes ◽  
Very High

<p>In general terms, climate adaptation in cities is highly complicated by the very high required spatial and temporal resolution. The high resolution is needed to capture both the full variability of small-scale high-impact weather phenomena and the associated response from the mosaic of land uses and buildings in urban environments. Most commonly available climate model simulations and projections are too spatially coarse (≥10 km) for a proper assessment of many important urban climate impacts. </p><p>In terms of water-related impacts, a key issue concerns the reproduction of local short-duration rainfall extremes (cloudbursts) that may cause pluvial flooding. An accurate reproduction of the convective generation of such extremes requires a spatial resolution of at least 5 km, preferably even higher, in convection-permitting regional climate models (CPRCM). Conceivably, estimates of future changes in cloudburst characteristics and associated statistics based on CPRCM simulations will be more reliable than today’s estimates based on non-CP RCMs. Because of the extreme computational demand, however, the number of CPRCM simulations made is still rather low and generally limited to small domains and/or short time slices.</p><p>But many efforts are currently being made in this direction and the main focus of this presentation will be a case study evaluation of hourly rainfall extremes from 3×3 km² convection-permitting simulations with the HARMONIE-climate model over the Nordic region. The case study will focus on the region around the Öresund strait, that connects southern Sweden and eastern Denmark. This region contains the cities Malmö and Copenhagen that were both hit by heavy cloudburst in the last decade, that caused severe flooding and substantial damage to infrastructure.</p><p>The presentation will include different aspects of the simulations and their applicability:</p><ul><li><em>Historical performance.</em> Evaluation of reference period simulations, with both ERA-Interim and GCM boundaries, against high-resolution observations, focusing at the reproduction of short-duration (sub-daily) extremes but also e.g. diurnal cycle and spatial variability.</li> <li><em>Future changes.</em> Assessment in terms of climate factors for different durations, return periods and future time horizons. A comparison is made with climate factors estimated from lower-resolution, non-convection permitting downscalings based on the same GCM projections.</li> <li><em>End-user practices.</em> A discussion of what resolution that is needed in order to meet different stakeholders’ needs in the light of climate adaptation. The key question is how the output from CPRCM simulations can be processed and interpreted to provide an added value. </li> </ul><p>Besides the above analyses, two additional related investigations will be presented:</p><ul><li>Lessons learnt from experiments of tailored “urban downscaling” of climate projections down to 1×1 km² and 15 min over selected European urban regions (Stockholm, Bologna, Amsterdam) performed in the Urban SIS project.</li> <li>An evaluation of hourly rainfall extremes over selected European countries in a 11×11 km² EURO-CORDEX ensemble, including spatial patterns and temperature scaling of the estimated future changes.</li> </ul>

scholarly journals A 40-year global data set of visible-channel remote-sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Vol 10 (4) ◽  
pp. 2043-2054 ◽  
Author(s):  
Benjamin Roger Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Data Set ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Very High

Abstract. A consistently calibrated 40-year-long data set of visible-channel remote-sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time series. The data set uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record for top-of-atmosphere (TOA) visible-channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote-sensing reflectance. The resulting data set is produced at daily, and archived at monthly, resolution, on a 0.1∘×0.1∘ grid at https://doi.org/10.1594/PANGAEA.892175. The primary aim of deriving this data set is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania huxleyi (where lith concentration >2–5×104 mL−1) over the past 40 years.

Statistical Downscaling for Climate Science

2019 ◽  
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.

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 Added value of high-resolution climate projections over South Korea on the scaling of precipitation with temperature

2021 ◽  
Author(s):  
Liying Qiu ◽  
Eun-Soon Im
Keyword(s):  
Global Warming ◽  
High Resolution ◽  
South Korea ◽  
Spatial Pattern ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Added Value ◽  
Climate Projections

Abstract This study evaluates the resolution dependency of scaling precipitation with temperature from the perspective of the added value of high-resolution (5-km) dynamical downscaling using various kinds of long-term climate change projections over South Korea. Three CMIP5 Global Climate Models (GCMs) with different climate sensitivities, and one pseudo global warming (PGW) experiment, are downscaled by Weather Research and Forecasting (WRF) one-way double nested modeling system with convective parameterization for the reference (1976-2005) and future (2071-2100) periods under RCP8.5 scenario. A detailed comparison of the driving GCM/PGW, 20-km mother simulation, and 5-km nested simulation demonstrates improved representation of precipitation with increasing resolution not only in the spatial pattern and magnitude for both the mean and the extremes, but also in a more realistic representation of extreme precipitation’s sensitivities to temperature. According to the projected precipitation changes downscaled from both GCM ensemble and PGW, there will be intensified precipitation, particularly for the extremes, over South Korea under the warming, which is primarily contributed by CP increase that shows higher temperature sensitivity. This study also compares the extreme precipitation-temperature scaling relations within-epoch (apparent scaling) and between-epoch (climate scaling). It confirms that the magnitude and spatial pattern of the two scaling rates can be quite different, and the precipitation change over Korea under global warming is mainly controlled by thermodynamic factors.

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