scholarly journals On the sensitivity of meteorological forcing resolution on hydrologic metrics

2019 ◽  
Author(s):  
Fadji Z. Maina ◽  
Erica R. Siirila-Woodburn ◽  
Pouya Vahmani
Keyword(s):  
Sierra Nevada ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Central Valley ◽  
Snow Accumulation ◽  
Watershed Model ◽  
Meteorological Forcing ◽  
Groundwater Levels

Abstract. Projecting the spatio-temporal changes to water resources under a no-analog future climate requires physically-based integrated hydrologic models, which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, typically have a resolution of several kilometers, while hydrologic metrics of interest (e.g. discharge, groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution watershed model is forced with various resolutions (0.5 to 40.5 km) of meteorological forcing generated by a dynamical downscaling analysis based on a regional climate model (WRF) to assess how the uncertainties associated with the spatial resolution of meteorological forcing affect the simulated hydrology. The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare un-dammed river basin and is used here as a testbed to illustrate potential impacts on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that localized biases in groundwater levels can be as large as 5–10 m and that other metric biases (e.g. ET and snowpack dynamics) are seasonally and spatially-dependent, but can have serious implications for model calibration and ultimately water management decisions.

scholarly journals Sensitivity of meteorological-forcing resolution on hydrologic variables

2020 ◽  
Vol 24 (7) ◽  
pp. 3451-3474
Author(s):  
Fadji Z. Maina ◽  
Erica R. Siirila-Woodburn ◽  
Pouya Vahmani
Keyword(s):  
Sierra Nevada ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Hydrologic Model ◽  
Snow Accumulation ◽  
Test Bed ◽  
Meteorological Forcing ◽  
Groundwater Levels

Abstract. Projecting the spatiotemporal changes in water resources under a no-analog future climate requires physically based integrated hydrologic models which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, are typically only resolved on the order of tens to hundreds of kilometers, while hydrologic variables of interest (e.g., discharge and groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution hydrologic model is forced with various resolutions of meteorological forcing (0.5 to 40.5 km) generated by a dynamical downscaling analysis from the regional climate model Weather Research and Forecasting (WRF). The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare undammed river basin and is used here as a test bed to illustrate potential impacts of various resolutions of meteorological forcing on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that the errors in spatial distribution patterns impact land surface processes and can be delayed in time. Localized biases in groundwater levels can be as large as 5–10 m and 3 m in surface water. Most hydrologic variables reveal that biases are seasonally and spatially dependent, which can have serious implications for model calibration and ultimately water management decisions.

scholarly journals Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1339-1357 ◽  
Author(s):  
Peter B. Gibson ◽  
Duane E. Waliser ◽  
Huikyo Lee ◽  
Baijun Tian ◽  
Elias Massoud
Keyword(s):  
United States ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Precipitation Indices

Abstract Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Modelling the Impacts of Climate Change on Shallow Groundwater Conditions in Hungary

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 668
Author(s):  
Attila Kovács ◽  
András Jakab
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Groundwater Resources ◽  
Regional Climate ◽  
Shallow Groundwater ◽  
Predictive Modelling ◽  
Climate Impacts ◽  
Annual Rainfall ◽  
Groundwater Levels

The purpose of the present study was to develop a methodology for the evaluation of direct climate impacts on shallow groundwater resources and its country-scale application in Hungary. A modular methodology was applied. It comprised the definition of climate zones and recharge zones, recharge calculation by hydrological models, and the numerical modelling of the groundwater table. Projections of regional climate models for three different time intervals were applied for the simulation of predictive scenarios. The investigated regional climate model projections predict rising annual average temperature and generally dropping annual rainfall rates throughout the following decades. Based on predictive modelling, recharge rates and groundwater levels are expected to drop in elevated geographic areas such as the Alpokalja, the Eastern parts of the Transdanubian Mountains, the Mecsek, and Northern Mountain Ranges. Less significant groundwater level drops are predicted in foothill areas, and across the Western part of the Tiszántúl, the Duna-Tisza Interfluve, and the Szigetköz areas. Slightly increasing recharge and groundwater levels are predicted in the Transdanubian Hills and the Western part of the Transdanubian Mountains. Simulation results represent groundwater conditions at the country scale. However, the applied methodology is suitable for simulating climate change impacts at various scales.

scholarly journals Comparison of Methods to Estimate Snow Water Equivalent at the Mountain Range Scale: A Case Study of the California Sierra Nevada

2017 ◽  
Vol 18 (4) ◽  
pp. 1101-1119 ◽  
Author(s):  
Melissa L. Wrzesien ◽  
Michael T. Durand ◽  
Tamlin M. Pavelsky ◽  
Ian M. Howat ◽  
Steven A. Margulis ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Mountain Range ◽  
Order Of Magnitude ◽  
Snow Water

Abstract Despite the importance of snow in global water and energy budgets, estimates of global mountain snow water equivalent (SWE) are not well constrained. Two approaches for estimating total range-wide SWE over Sierra Nevada, California, are assessed: 1) global/hemispherical models and remote sensing and models available for continental United States (CONUS) plus southern Canada (CONUS+) available to the scientific community and 2) regional climate model simulations via the Weather Research and Forecasting (WRF) Model run at 3, 9, and 27 km. As no truth dataset provides total mountain range SWE, these two approaches are compared to a “reference” SWE consisting of three published, independent datasets that utilize/validate against in situ SWE measurements. Model outputs are compared with the reference datasets for three water years: 2005 (high snow accumulation), 2009 (average), and 2014 (low). There is a distinctive difference between the reference/WRF datasets and the global/CONUS+ daily estimates of SWE, with the former suggesting up to an order of magnitude more snow. Results are qualitatively similar for peak SWE and 1 April SWE for all three years. Analysis of SWE time series indicates that lower SWE for global and CONUS+ datasets is likely due to precipitation, rain/snow partitioning, and ablation parameterization differences. It is found that WRF produces reasonable (within 50%) estimates of total mountain range SWE in the Sierra Nevada, while the global and CONUS+ datasets underestimate SWE.

scholarly journals The future climate characteristics of the Carpathian Basin based on a regional climate model mini-ensemble

2011 ◽  
Vol 6 (1) ◽  
pp. 69-73 ◽  
Author(s):  
I. Krüzselyi ◽  
J. Bartholy ◽  
A. Horányi ◽  
I. Pieczka ◽  
R. Pongrácz ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Statistical Significance ◽  
Regional Climate ◽  
Carpathian Basin ◽  
Climate Projections ◽  
Target Area ◽  
Max Planck ◽  
The Future

Abstract. Four regional climate models (RCMs) were adapted in Hungary for the dynamical downscaling of the global climate projections over the Carpathian Basin: (i) the ALADIN-Climate model developed by Météo France on the basis of the ALADIN short-range modelling system; (ii) the PRECIS model available from the UK Met Office Hadley Centre; (iii) the RegCM model originally developed at the US National Center for Atmospheric Research, is maintained at the International Centre for Theoretical Physics in Trieste; and (iv) the REMO model developed by the Max Planck Institute for Meteorology in Hamburg. The RCMs are different in terms of dynamical model formulation, physical parameterisations; moreover, in the completed simulations they use different spatial resolutions, integration domains and lateral boundary conditions for the scenario experiments. Therefore, the results of the four RCMs can be considered as a small ensemble providing information about various kinds of uncertainties in the future projections over the target area, i.e., Hungary. After the validation of the temperature and precipitation patterns against measurements, mean changes and some extreme characteristics of these patterns (including their statistical significance) have been assessed focusing on the periods of 2021–2050 and 2071–2100 relative to the 1961–1990 model reference period. The ensemble evaluation indicates that the temperature-related changes of the different RCMs are in good agreement over the Carpathian Basin and these tendencies manifest in the general warming conditions. The precipitation changes cannot be identified so clearly: seasonally large differences can be recognised among the projections and between the two periods. An overview is given about the results of the mini-ensemble and special emphasis is put on estimating the uncertainties in the simulations for Hungary.

Transient Future Climate over the Western United States Using a Regional Climate Model

2005 ◽  
Vol 9 (11) ◽  
pp. 1-21 ◽  
Author(s):  
Mark A. Snyder ◽  
Lisa C. Sloan
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Snow Accumulation ◽  
Global Climate Models ◽  
Coupled Gcm ◽  
Fully Coupled

Abstract Regional climate models (RCMs) have improved our understanding of the effects of global climate change on specific regions. The need for realistic forcing has led to the use of fully coupled global climate models (GCMs) to produce boundary conditions for RCMs. The advantages of using fully coupled GCM output is that the global-scale interactions of all components of the climate system (ocean, sea ice, land surface, and atmosphere) are considered. This study uses an RCM, driven by a fully coupled GCM, to examine the climate of a region centered over California for the time periods 1980–99 and 2080–99. Statistically significant increases in mean monthly temperatures by up to 7°C are found for the entire state. Large changes in precipitation occur in northern California in February (increase of up to 4 mm day−1 or 30%) and March (decrease of up to 3 mm day−1 or 25%). However, in most months, precipitation changes between the cases were not statistically significant. Statistically significant decreases in snow accumulation of over 100 mm (50%) occur in some months. Temperature increases lead to decreases in snow accumulation that impact the hydrologic budget by shifting spring and summer runoff into the winter months, reinforcing results of other studies that used different models and driving conditions.

scholarly journals Conditioning model output statistics of regional climate model precipitation on circulation patterns

2012 ◽  
Vol 19 (6) ◽  
pp. 623-633 ◽  
Author(s):  
F. Wetterhall ◽  
F. Pappenberger ◽  
Y. He ◽  
J. Freer ◽  
H. L. Cloke
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Direct Method ◽  
Regional Climate ◽  
Global Climate Models ◽  
Model Output ◽  
Small Catchment ◽  
Model Output Statistics

Abstract. Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.

scholarly journals Dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

2021 ◽  
Author(s):  
Yuan Qiu ◽  
Jinming Feng ◽  
Zhongwei Yan ◽  
Jun Wang
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Climate Changes ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
High Elevation ◽  
Global Climate Models ◽  
Climate Projections

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

scholarly journals Assessing Past Climate Biases and the Added Value of CORDEX-CORE Precipitation Simulations over Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2058
Author(s):  
Gnim Tchalim Gnitou ◽  
Guirong Tan ◽  
Ruoyun Niu ◽  
Isaac Kwesi Nooni
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Added Value ◽  
Spatial Coverage ◽  
Wide Range

The present study investigates the skills of CORDEX-CORE precipitation outputs in simulating Africa’s key seasonal climate features, emphasizing the added value (AV) of the dynamical downscaling approach from which they were derived. The results indicate the models’ good skills in capturing African rainfall patterns and dynamics at satellite-based observation resolutions, with up to 65.17% significant positive AV spatial coverage for the CCLM5 model and up to 55.47% significant positive AV spatial coverage for the REMO model. Unavoidable biases are however present in rainfall-abundant areas and are reflected in the AV results, but vary based on the season, the sub-area, and the Global Climate Model–Regional Climate Models (GCM-RCM) combination considered. The RCMs’ ensemble mean generally performs better than individual GCM–RCM simulations. A further analysis of the GCM–RCM model chain indicates a strong influence of the dynamical downscaling approach on the driving GCMs. However, exceptions are found in some seasons for specific RCMs’ outputs, where GCMs are influential. The findings also revealed that observational uncertainties can influence AV and contribute to a 6 to 34% difference in significant positive AV spatial coverage results. An analysis of these results suggests that the AV by CORDEX-CORE simulations over Africa depend on how well the GCM physics are integrated to those of the RCMs and how these features are accommodated in the high-resolution setting of the downscaling experiments. The deficiencies of the CORDEX-CORE simulations could be related to how well key processes are represented within the RCM models. For Africa, these results show that CORDEX-CORE products could be adequate for a wide range of high-resolution precipitation data applications.

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