Technical challenges and solutions in representing lakes when using WRF in downscaling applications

Abstract. The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional downscaled fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

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Technical challenges and solutions in representing lakes when using WRF in downscaling applications

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-7121-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 7121-7150 ◽

Cited By ~ 1

Author(s):

M. S. Mallard ◽

C. G. Nolte ◽

T. L. Spero ◽

O. R. Bullock ◽

K. Alapaty ◽

...

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Wrf Model ◽

Ice Cover ◽

Alternative Methods ◽

Weather Research And Forecasting ◽

Inland Lakes ◽

Air Temperatures

Abstract. The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional downscaled fields, inland lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

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Regional Decadal Climate Predictions Using an Ensemble of WRF Parameterizations Driven by the MIROC5 GCM

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0051.1 ◽

2019 ◽

Vol 58 (3) ◽

pp. 527-549 ◽

Cited By ~ 2

Author(s):

Ehud Strobach ◽

Golan Bel

Keyword(s):

Surface Layer ◽

Surface Temperature ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Wrf Model ◽

Maximum Surface ◽

Surface Temperatures

AbstractRegional climate models (RCMs) are expected to provide better representations of the climate dynamics because of their higher spatial resolutions. Here, we generated an ensemble of decadal (2006–36) RCM predictions for the area of Israel, which spans a considerable climatic gradient and comprises complex terrain. We used the WRF Model forced by the MIROC5 global climate model (GCM). The ensemble was generated by choosing different combinations of radiation, microphysics, surface layer, and planetary boundary layer parameterizations. The simulation results were compared with meteorological station data for the first simulated decade. For the minimum surface temperature, all the RCM configurations performed better than the driving GCM, while for the maximum surface temperature, only three out of eight configurations improved the predictions. The RCM configurations had higher errors in predicting the precipitation, but four configurations had comparable errors to the GCM. For the next two decades, the ensemble average predicts an increase of 0.51° and 0.40°C decade−1 for the average daily minimum and maximum surface temperatures, respectively. No significant change is predicted in the precipitation. We found that all the parameterizations affect the predictions of the surface temperatures and precipitation [e.g., the CAM radiation scheme simulates colder temperatures than the RRTM for GCMs (RRTMG)] but the PBL and surface layer scheme has the largest effect on the errors. Spectral nudging was found to have a considerable effect on the deviations of the precipitation predicted by the WRF configurations from the predictions of the GCM and a much smaller effect on the surface temperature predictions.

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Downscaling Atmosphere-Ocean Global Climate Model Precipitation Simulations over Africa Using Bias-Corrected Lateral and Lower Boundary Conditions

Atmosphere ◽

10.3390/atmos9120493 ◽

2018 ◽

Vol 9 (12) ◽

pp. 493 ◽

Cited By ~ 2

Author(s):

Leonard Druyan ◽

Matthew Fulakeza

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Lower Boundary ◽

Climate Projections ◽

African Easterly Jet ◽

Modèle Simulation ◽

Goddard Institute ◽

Bias Corrections

A prequel study showed that dynamic downscaling using a regional climate model (RCM) over Africa improved the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM: ModelE) simulation of June–September rainfall patterns over Africa. The current study applies bias corrections to the lateral and lower boundary data from the AOGCM driving the RCM, based on the comparison of a 30-year simulation to the actual climate. The analysis examines the horizontal pattern of June–September total accumulated precipitation, the time versus latitude evolution of zonal mean West Africa (WA) precipitation (showing monsoon onset timing), and the latitude versus altitude cross-section of zonal winds over WA (showing the African Easterly Jet and the Tropical Easterly Jet). The study shows that correcting for excessively warm AOGCM Atlantic sea-surface temperatures (SSTs) improves the simulation of key features, whereas applying 30-year mean bias corrections to atmospheric variables driving the RCM at the lateral boundaries does not improve the RCM simulations. We suggest that AOGCM climate projections for Africa should benefit from downscaling by nesting an RCM that has demonstrated skill in simulating African climate, driven with bias-corrected SST.

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Downscaling a global climate model to simulate climate change over the US and the implication on regional and urban air quality

Geoscientific Model Development ◽

10.5194/gmd-6-1429-2013 ◽

2013 ◽

Vol 6 (5) ◽

pp. 1429-1445 ◽

Cited By ~ 28

Author(s):

M. Trail ◽

A. P. Tsimpidi ◽

P. Liu ◽

K. Tsigaridis ◽

Y. Hu ◽

...

Keyword(s):

Climate Change ◽

Air Quality ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Climate Conditions ◽

Southeastern Us ◽

The Us ◽

Ozone Levels

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with the Weather Research and Forecasting (WRF) model to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the contiguous United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF regional climate model (RCM) to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high-resolution simulations produce somewhat different results than the coarse-resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (western US, Texas, northeastern, and southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). Changes in regional climate that would enhance ozone levels are increased temperatures and stagnation along with decreased precipitation and ventilation. We also find that daily peak temperatures tend to increase in most major cities in the US, which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

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Disentangling effects of uncertainties on population projections: climate change impact on an epixylic bryophyte

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2012.0428 ◽

2012 ◽

Vol 279 (1740) ◽

pp. 3098-3105 ◽

Cited By ~ 12

Author(s):

Alejandro Ruete ◽

Wei Yang ◽

Lars Bärring ◽

Nils Chr. Stenseth ◽

Tord Snäll

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Population Decline ◽

Population Projections ◽

Viability Analysis ◽

Uncertainty Sources ◽

Population Dynamics Model ◽

Sres Scenarios

Assessment of future ecosystem risks should account for the relevant uncertainty sources. This means accounting for the joint effects of climate variables and using modelling techniques that allow proper treatment of uncertainties. We investigate the influence of three of the IPCC's scenarios of greenhouse gas emissions (special report on emission scenarios (SRES)) on projections of the future abundance of a bryophyte model species. We also compare the relative importance of uncertainty sources on the population projections. The whole chain global climate model (GCM)—regional climate model—population dynamics model is addressed. The uncertainty depends on both natural- and model-related sources, in particular on GCM uncertainty. Ignoring the uncertainties gives an unwarranted impression of confidence in the results. The most likely population development of the bryophyte Buxbaumia viridis towards the end of this century is negative: even with a low-emission scenario, there is more than a 65 per cent risk for the population to be halved. The conclusion of a population decline is valid for all SRES scenarios investigated. Uncertainties are no longer an obstacle, but a mandatory aspect to include in the viability analysis of populations.

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Brief Communication: An update of the article "Modelling flood damages under climate change conditions – a case study for Germany"

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-1617-2016 ◽

2016 ◽

Vol 16 (7) ◽

pp. 1617-1622 ◽

Cited By ~ 8

Author(s):

Fred Fokko Hattermann ◽

Shaochun Huang ◽

Olaf Burghoff ◽

Peter Hoffmann ◽

Zbigniew W. Kundzewicz

Keyword(s):

Climate Change ◽

Large Scale ◽

Considerable Increase ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Impact Modelling ◽

Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood-related losses can be expected in a future warmer climate. However, the general significance of the study was limited by the fact that outcome of only one global climate model (GCM) was used as a large-scale climate driver, while many studies report that GCMs are often the largest source of uncertainty in impact modelling. Here we show that a much broader set of global and regional climate model combinations as climate drivers show trends which are in line with the original results and even give a stronger increase of damages.

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Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽

10.3390/ecws-3-05819 ◽

2018 ◽

Vol 7 (1) ◽

pp. 23 ◽

Cited By ~ 1

Author(s):

Carlos Garijo ◽

Luis Mediero

Keyword(s):

Climate Change ◽

Iberian Peninsula ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Control Period ◽

Regional Climate Models ◽

The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

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Internal Variability of the Canadian RCM’s Hydrological Variables at the Basin Scale in Quebec and Labrador

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-051.1 ◽

2012 ◽

Vol 13 (2) ◽

pp. 443-462 ◽

Cited By ~ 21

Author(s):

Marco Braun ◽

Daniel Caya ◽

Anne Frigon ◽

Michel Slivitzky

Keyword(s):

Climate Model ◽

Model Simulation ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Internal Variability ◽

Spectral Nudging ◽

Model Driven ◽

Basin Scale ◽

Hydrological Variables

Abstract The effect of a regional climate model’s (RCM’s) internal variability (IV) on climate statistics of annual series of hydrological variables is investigated at the scale of 21 eastern Canada watersheds in Quebec and Labrador. The analysis is carried out on 30-yr pairs of simulations (twins), performed with the Canadian Regional Climate Model (CRCM) for present (reanalysis and global climate model driven) and future (global climate model driven) climates. The twins differ only by the starting date of the regional simulation—a standard procedure used to trigger internal variability in RCMs. Two different domain sizes are considered: one comparable to domains used for RCM simulations over Europe and the other comparable to domains used for North America. Results for the larger North American domain indicate that mean relative differences between twin pairs of 30-yr climates reach ±5% when spectral nudging is used. Larger differences are found for extreme annual events, reaching about ±10% for 10% and 90% quantiles (Q10 and Q90). IV is smaller by about one order of magnitude in the smaller domain. Internal variability is unaffected by the period (past versus future climate) and by the type of driving data (reanalysis versus global climate model simulation) but shows a dependence on watershed size. When spectral nudging is deactivated in the large domain, the relative difference between pairs of 30-yr climate means almost doubles and approaches the magnitude of a global climate model’s internal variability. This IV at the level of the natural climate variability has a profound impact on the interpretation, analysis, and validation of RCM simulations over large domains.

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Quantification of terrestrial regional climate variability from a large database of pollen-based reconstructions and implications for future projections

10.5194/egusphere-egu2020-1203 ◽

2020 ◽

Author(s):

Raphael Hébert ◽

Ulrike herzschuh ◽

Thomas Laepple

Keyword(s):

Climate Variability ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Irregular Sampling ◽

Large Database ◽

Spatial Correlation Structure ◽

Pollen Records

Multidecadal to millenial timescale climate variability has been investigated over the oceanusing extensive proxy data and it was found to yield coherent interproxy estimates of global and regional sea-surface temperature (SST) climate variability (Laepple and Huybers, 2014). Global Climate Model (GCM) simulations on the other hand, were found to exhibit an increasingly large deficit of regional SST climate variability for increasingly longer timescales.Further investigation is needed to better quantify terrestrial climate variability for longtimescales and validate climate models.Vegetation related proxies such as tree rings and pollen records are the most widespreadtypes of archives available to investigate terrestrial climate variability. Tree ring records areparticularly useful for short time scales estimates due to their annual resolution, while pollen-based reconstructions are necessary to cover the longer timescales. In the present work, we use a large database of 1873 pollen records covering the northern hemisphere in order to quantify Holocene vegetation and climate variability for the first time at centennial to multi-millenial timescales.To ensure the robustness of our results, we are particularly interested in the spatio-temporal representativity of the archived signal in pollen records after taking into account the effective spatial scale, the intermittent and irregular sampling, the age-uncertainty and the sediment mixing effect. A careful treatment of the proxy formation allows us to investigate the spatial correlation structure of the pollen-based climate reconstructions as a function of timescales. The pollen data results are then contrasted with the analysis replicated using transient Holocene simulations produced with state-of-the-art climate models as well as stochastic climate model simulations.Our results indicate a substantial gap in terrestrial climate variability between the climate model simulations and the pollen reconstructions at centennial to multi-millenial timescales, mirroring the variability gap found in the marine domain. Finally, we investigate how future climate model projections with greater internal variability would be affected, and how this increases the uncertainty of regional land temperature projections.

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Coordinated Global and Regional Climate Modeling*

Journal of Climate ◽

10.1175/jcli-d-15-0161.1 ◽

2015 ◽

Vol 29 (1) ◽

pp. 17-35 ◽

Cited By ~ 102

Author(s):

J. F. Scinocca ◽

V. V. Kharin ◽

Y. Jiao ◽

M. W. Qian ◽

M. Lazare ◽

...

Keyword(s):

Regional Climate Model ◽

Climate Model ◽

Climate Modeling ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Value Added ◽

Regional Climate Modeling ◽

Climate Projections ◽

Climate Modelling

Abstract A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs’ value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.

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