Downscaled Reanalysis Data Research Articles

Response of lacustrine glacier dynamics to atmospheric forcing in the Cordillera Darwin

Abstract Calving glaciers respond quickly to atmospheric variability through ice dynamic adjustment. Particularly, single weather extremes may cause changes in ice-flow velocity and terminus position. Occasionally, this can lead to substantial event-driven mass loss at the ice front. We examine changes in terminus position, ice-flow velocity, and calving flux at the grounded lacustrine Schiaparelli Glacier in the Cordillera Darwin using geo-referenced time-lapse camera images and remote sensing data (Sentinel-1) from 2015 to 2022. Lake-level records, lake discharge measurements, and a coupled energy and mass balance model provide insight into the subglacial water discharge. We use downscaled reanalysis data (ERA5) to identify climate extremes and track land-falling atmospheric rivers to investigate the ice-dynamic response on possible atmospheric drivers. Meltwater controls seasonal variations in ice-flow velocity, with an efficient subglacial drainage system developing during the warm season and propagating up-glacier. Calving accounts for 4.2% of the ice loss. Throughout the year, warm spells, wet spells, and landfalling atmospheric rivers promote calving. The progressive thinning of the ice destabilizes the terminus position, highlighting the positive feedback between glacier thinning, near-terminus ice-flow acceleration, and calving flux.

Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay

Abstract. Several studies identified heterogeneous glacier mass changes in western High Mountain Asia over the last decades. Causes for these mass change patterns are still not fully understood. Modelling the physical interactions between glacier surface and atmosphere over several decades can provide insight into relevant processes. Such model applications, however, have data needs which are usually not met in these data-scarce regions. Exceptionally detailed glaciological and meteorological data exist for the Abramov Glacier in the Pamir Alay range. In this study, we use weather station measurements in combination with downscaled reanalysis data to force a coupled surface energy balance–multilayer subsurface model for Abramov Glacier for 52 years. Available in situ data are used for model calibration and validation. We find an overall negative mass balance of −0.27 mw.e.a-1 for 1968/1969–2019/2020 and a loss of firn pore space causing a reduction of internal accumulation. Despite increasing air temperatures, we do not find an acceleration of glacier-wide mass loss over time. Such an acceleration is compensated for by increasing precipitation rates (+0.0022 mw.e.a-1, significant at a 90 % confidence level). Our results indicate a significant correlation between annual mass balance and precipitation (R2 = 0.72).

Assessing impacts of climate change on hydrology in data-scarce Volta River Basin using downscaled reanalysis data

Assessing impacts of climate change on hydrology in data-scarce Volta River Basin using downscaled reanalysis data

Assessing impacts of climate change on hydrology in data-scarce Volta River Basin using downscaled reanalysis data

This study uses high resolution (0.3°~3 km) climate forecast system reanalysis (CFSR), SWAT and statistically downscaled A1B emission scenario to assess impacts of climate change on hydrology in data scarce Volta River Basin of West Africa. SWAT was calibrated to simulate runoff at six gauging stations within the basin. Using 1979-2008 as baseline, there is average increase in rainfall of 3.0% to 5.0%. Average rainfall in 2071-2100 is higher than 2041-2070 and 2011-2040 by 0.9% and 2.0%, respectively. Monthly runoff is projected to increase from February to August and decrease from September to January. Overall, the simulation reveals increased variability and increase of 12.0% in average annual runoff. The results give more insight into future water availability and provide quantitative data for incorporation into climate change adaptation policies for effective water resources management in the basin.

Integration of a Building Energy Model in an Urban Climate Model and its Application

We report the ability of an urban canopy model, coupled with a regional climate model, to simulate energy fluxes, the intra-urban variability of air temperature, urban-heat-island characteristics, indoor temperature variation, as well as anthropogenic heat emissions, in Berlin, Germany. A building energy model is implemented into the Double Canyon Effect Parametrization, which is coupled with the mesoscale climate model COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode) and takes into account heat generation within buildings and calculates the heat transfer between buildings and the urban atmosphere. The enhanced coupled urban model is applied in two simulations of 24-day duration for a winter and a summer period in 2018 in Berlin, using downscaled reanalysis data to a final grid spacing of 1 km. Model results are evaluated with observations of radiative and turbulent energy fluxes, 2-m air temperature, and indoor air temperature. The evaluation indicates that the improved model reproduces the diurnal characteristics of the observed turbulent heat fluxes, and considerably improves the simulated 2-m air temperature and urban heat island in winter, compared with the simulation without the building energy model. Our set-up also estimates the spatio–temporal variation of wintertime energy consumption due to heating with canyon geometry. The potential to save energy due to the urban heat island only becomes evident when comparing a suburban site with an urban site after applying the same grid-cell values for building and street widths. In summer, the model realistically reproduces the indoor air temperature and its temporal variation.

Snow climatology for the mountains in the Iberian Peninsula using satellite imagery and simulations with dynamically downscaled reanalysis data

AbstractThe presence of a seasonal snowpack determines the hydrology, geomorphology and ecology of wide parts of the Iberian Peninsula, with strong implications for the economy, transport and risk management. Thus, reliable information on snow is necessary from a scientific and operational point of view. This is the case of the Iberian Peninsula where, lack of observation has impeded proper analysis of snowpack duration, magnitude and interannual variability. In this study, we present the first snow climatology of the entire Iberian Peninsula. The scarcity of in situ observations has been overcome, using a newly developed remote sensing snow database from MODIS satellite sensors for the period 2000–2014 and a physically based snow model (Factorial Snow Model—FSM), driven by a regional atmospheric model (Weather Research and Forecast model—WRF) over the Iberian Peninsula for the period 1980–2014. The snowpack of the main mountain areas (Pyrenees, Cantabrian, Central, Iberian range and Sierra Nevada) are described, estimated from the generated databases. The information has been processed using a k‐means cluster algorithm, looking for similarities in snow indices at different elevation bands. Results show four different types of snowpack in terms of depth, duration and interannual variability, lying over different elevation bands in the different ranges, proving the variability of the snowpack over Iberia. Analyses reveal areas characterized by ephemeral snowpacks, while in some sectors snowpack lasts, on average, 198 days per year with 3.02 m of peak snow depth. The coefficient of variation of interannual peak snow depth oscillated between 35.2 and 162.4%. All the analysed indices show that at common elevations the Cantabrian range and the Pyrenees host the deepest and longest lasting snowpacks, followed by the Central and Iberian ranges. The Sierra Nevada exhibits the shortest, shallowest snowpack and more year‐to‐year variability.

An approach to simulate the climate-driven streamflow in the data-scarce mountain basins of Northwest China

With global warming, the inland river basin in the arid region of Northwest China is facing a serious water supply situation. The headwater basin of the inland river is located in the high-altitude mountainous region, and there are few meteorological observation sites, so it is difficult to apply distributed hydrological models and other models based on the physical mechanism of runoff generation to evaluate climate change and its impact on streamflow. To simulate the climate-driven streamflow in data-scarce mountain basins of Northwest China, we developed an integrated approach by using downscaled reanalysis data, Mann–Kendall test, ensemble empirical mode decomposition and backpropagation artificial neural networks together with the weights connection method. We validated the approach in the Kaidu River basin located in the Tianshan mountains. The results showed that the streamflow increased 12.9% by $$2.5 \times 10^{8}\,\hbox {m}^{3}$$ per decade with the warm and wet climate, while the average annual temperature increased 5.2% at a rate of $$0.3{^{\circ }}\hbox {C}$$ per decade and the precipitation increased 37.3% at a rate of 16.4 mm per decade during the period from 1980 to 2015. The impact of temperature variability on streamflow was 44.21 ± 2.08% and the impact of precipitation variability on streamflow was 55.79 ± 2.08%.

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Abstract. With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

Assessment of Alaska Rain-on-Snow Events Using Dynamical Downscaling

AbstractThe ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

Large-scale weather patterns favorable for volcanic smog occurrences on O’ahu, Hawai’i

Kīlauea Volcano, located on the Island of Hawai’i, released approximately 3700 t of sulfur dioxide (SO2) per day from April 2009 through 2014. Within the atmosphere, SO2 is oxidized and converted to sulfuric-acid aerosols, and this volcanic smog is commonly referred to as vog. This study focuses on large-scale weather patterns that bring vog to O’ahu. The Hawai’i State Department of Health PM2.5 measurements were used to identify elevated vog conditions, and a total of 101 vog days were found. European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis data were used to determine weather patterns. These 101 vog days were the result of 57 distinct vog events lasting from 3 h up to 4 days. The 57 events were further categorized into three large-scale weather patterns: pre-cold fronts (37 cases), upper-level disturbances (17 cases), and Kona lows (3 cases). The pre-cold front events had variable duration lasting up to 4 days, and the largest vog concentrations occurred during long-duration pre-cold front events. Trade winds did not transport vog to O’ahu. As part of this effort, ERA-Interim data were downscaled to a resolution of 10 km and then 3.3 km using the Weather Research and Forecasting (WRF) model. The downscaled reanalysis data were used as input by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The HYSPLIT model allowed for a visual representation of how vog is advected by large-scale wind patterns.

Mass Balance Evolution of Black Rapids Glacier, Alaska, 1980–2100, and Its Implications for Surge Recurrence

Surge-type Black Rapids Glacier, Alaska, has undergone strong retreat since it last surged in 1936-37. To assess its evolution during the late 20th and 21st centuries and determine potential implications for surge likelihood, we run a simplified glacier model over the periods 1980-2015 (hindcasting) and 2015-2100 (forecasting). The model is forced by daily temperature and precipitation fields, with downscaled reanalysis data used for the hindcasting. A constant climate scenario and an RCP 8.5 scenario based on the GFDL-CM3 climate model are employed for the forecasting. Debris evolution is accounted for by a debris layer time series derived from satellite imagery (hindcasting) and a parametrized debris evolution model (forecasting). A retreat model accounts for the evolution of the glacier geometry. Model calibration, validation and parametrization rely on an extensive set of in situ and remotely sensed observations. To explore uncertainties in our projections, we run the glacier model in a Monte Carlo fashion, varying key model parameters and input data within plausible ranges. Our results for the hindcasting period indicate a negative mass balance trend, caused by atmospheric warming in the summer, precipitation decrease in the winter and surface elevation lowering (climate-elevation feedback), which exceed the moderating effects from increasing debris cover and glacier retreat. Without the 2002 rockslide deposits on Black Rapids' lower reaches, the mass balances would be more negative, by ~20% between the 2003 and 2015 mass-balance years. Despite its retreat, Black Rapids Glacier is substantially out of balance with the current climate. By 2100, ~8% of Black Rapids' 1980 area are projected to vanish under the constant climate scenario and ~73% under the RCP 8.5 scenario. For both scenarios, the remaining glacier portions are out of balance, suggesting continued retreat after 2100. Due to mass starvation, a surge in the 21st century is unlikely. The projected retreat will affect the glacier's runoff and change the landscape in the Black Rapids area markedly.

Temperature suitability for malaria climbing the Ethiopian Highlands

While the effect of climate change on the prevalence of malaria in the highlands of Eastern Africa has been the topic of protracted debate, temperature is widely accepted as a fundamentally important environmental factor constraining its transmission. Air temperatures below approximately 18 °C and 15 °C, respectively, prohibit the development of the Plasmodium falciparum and P. vivax parasites responsible for the majority of malaria cases in Ethiopia. Low temperatures also impede the development rates of the Anopheles mosquito vectors. While locations of sufficiently high elevation have temperatures below these transmission thresholds, a fundamental question is how such temperature ‘threshold elevations’ are changing with time. A lack of high quality, high spatial resolution climate data has previously prohibited a rigorous investigation. Using a newly developed national temperature dataset for Ethiopia that combines numerous in-situ surface observations with downscaled reanalysis data, we here identify statistically significant increases in elevation for both the 18 °C and 15 °C thresholds in highland areas between 1981–2014. Substantial interannual and spatial variations in threshold elevations are identified, the former associated with the El Niño Southern-Oscillation and the latter with the complex climate of the region. The estimated population in locations with an upward trend in the 15 °C threshold elevation is approximately 6.5 million people (2.2 million for 18 °C). While not a direct prediction of the additional population made vulnerable to malaria through a shift to higher temperature, our results underscore a newly acquired ability to investigate climate variability and trends at fine spatial scales across Ethiopia, including changes in a fundamental constraint on malaria transmission in the Ethiopian Highlands.

Quantifying mass balance processes on the Southern Patagonia Icefield

Abstract. We present surface mass balance simulations of the Southern Patagonia Icefield (SPI) driven by downscaled reanalysis data. The simulations were evaluated and interpreted using geodetic mass balances, measured point balances and a complete velocity field of the icefield for spring 2004. The high measured accumulation of snow of up to 15.4 m w.e. yr−1 (meters water equivalent per year) as well as the high measured ablation of up to 11 m w.e. yr−1 is reproduced by the model. The overall modeled surface mass balance was positive and increasing during 1975–2011. Subtracting the surface mass balance from geodetic balances, calving fluxes were inferred. Mass losses of the SPI due to calving were strongly increasing from 1975–2000 to 2000–2011 and higher than losses due to surface melt. Calving fluxes were inferred for the individual glacier catchments and compared to fluxes estimated from velocity data. Measurements of ice thickness and flow velocities at the glaciers' front and spatially distributed accumulation measurements can help to reduce the uncertainties of the different terms in the mass balance of the Southern Patagonia Icefield.

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002–2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.

Climate change scenarios for temperature and precipitation in Aragón (Spain)

By applying a two-step statistical downscaling technique to four climate models under different future emission scenarios, we produced future projections of the daily precipitation and the maximum and minimum temperatures over the Spanish region of Aragón. The reliability of the downscaling technique was assessed by a verification process involving the comparison of the downscaled reanalysis data with the observed data — the results were very good for the temperature and acceptable for the precipitation. To determine the ability of the climate models to simulate the real climate, their simulations of the past (the 20C3M output) were downscaled and then compared with the observed climate. The results are quite robust for temperature and less conclusive for the precipitation. The downscaled future projections exhibit a significant increase during the entire 21st century of the maximum and minimum temperatures for all the considered IPCC future emission scenarios (A2, A1B, B1), both for mid-century (increases relative to the 1971–2000 averages between 1.5°C and 2.5°C, depending on the scenario) and for the end of the century (for the maximum temperature of approximately 3.75°C, 3.3°C, and 2.1°C for A2, A1B, and B1 scenarios respectively, and for the minimum temperature of 3.1°C, 2.75°C, and 1.75°C). The precipitation does not follow such a clear tendency (and exhibits greater uncertainties), but all the scenarios suggest a moderate decrease in rainfall for the mid-century (2–4%) and for the end of the century (4.5–5.5%). Due to the clear spatial differences in climate characteristics, we divided the studied area into five sub-regions to analyse the different changes on these areas; we determined that the high mountains (Pyrenees, Mediterranean-Oceanic transitional climate) and the lands of the Ebro River Basin (Continental sub-Mediterranean climate) will probably be the most affected.

Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

Abstract. In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental- or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model

The goal of this study was to evaluate the ability of dynamically downscaled reanalysis data to reproduce local-scale spatiotemporal precipitation and temperature data needed to accurately predict streamflow in the Tampa Bay region of west central Florida. In particular, the Florida State University Center for Ocean-Atmospheric Prediction Studies CLARReS10 data (NCEP DOE 2 reanalysis data (R2) downscaled to 10-km over the Southeast USA using the Regional Spectral Model (RSM) were evaluated against locally available observed precipitation and temperature data and then used to drive an integrated hydrologic model that was previously calibrated for the Tampa Bay region. Resulting streamflow simulations were evaluated against observed data and previously calibrated model results. Results showed that the raw downscaled reanalysis predictions accurately reproduced the seasonal trends of mean daily minimum temperature, maximum temperature and precipitation, but generally overestimated the monthly mean and standard deviation of daily precipitation. Biases in the temporal mean and standard deviation of daily precipitation and temperature predictions were effectively removed using a CDF-mapping approach; however, errors in monthly precipitation totals remained after bias correction. Monthly streamflow simulation error statistics indicated that the accuracy of the streamflow produced by the bias-corrected downscaled reanalysis data was satisfactory (i.e., sufficient for seasonal to decadal planning), but that the accuracy of the streamflow produced by the raw downscaled reanalysis data was unsatisfactory for water resource planning purposes. The findings of this study thus indicate that further improvement in large-scale reanalysis data and regional climate models is needed before dynamically downscaled reanalysis data can be used directly (i.e. without bias correction with local data) to drive hydrologic models. However, bias-corrected dynamically downscaled data show promise for extending local historic climate observation records for hydrologic simulations. Furthermore, results of this study indicate that similarly bias-corrected dynamically downscaled retrospective and future GCM projections should be suitable for assessing potential hydrologic impacts of future climate change in the Tampa Bay region.

Time trends and variations in mean and accumulated solar radiation for the ripening period of paddy rice in Kyushu for 1979-2007

A notable declining trend in the eating quality of rice panicles has been observed in Kyushu, Western Japan, since the 1990's. As solar radiation is one of the factors determining eating quality, this study investigated the recent time trends and variations in the mean and accumulated radiation for the ripening period of paddy rice in Kyushu during the period 1979-2007. The 3-year running mean radiation data, which are dynamically downscaled reanalysis data using the non-hydrostatic regional climate model (NHRCM) with a grid interval of 20 km, were used for analysis after the validation. From a meta-analysis of governmental crop statistics, it was found that the ripening period (i.e., the period between heading and harvesting) in Kyushu was shortened by 10 days in a 29-year period and generally occurred between the end of August and early October in the 2000's but between early September and the end of October in the 1980's. The change in the length and timing of the ripening period resulted in decreased accumulated radiation for the period, although the mean radiation during the period increased as a result of the earlier timing of the ripening period. The empirical orthogonal function (EOF) analysis results showed that the earlier timing and shorter ripening period are the most dominant factors explaining the radiation change during the ripening period in Kyushu in the past three decades. In addition, a more westward extension in Pacific anticyclones and an associated change in the locations of precipitation that decreased the mean and accumulated radiation for the ripening period in the area were frequently observed in the 2000's. These results indicate a more adverse radiation condition for paddy rice production in the 2000's compared to the 1980's and 1990's.

An analysis of observed daily maximum wind gusts in the UK

The greatest attention to the UK wind climatology has focused upon mean windspeeds, despite a knowledge of gust speeds being essential to a variety of users. This paper goes some way to redressing this imbalance by analysing observed daily maximum gust speeds from a 43-station network over the period 1980–2005. Complementing these data are dynamically downscaled reanalysis data, generated using the PRECIS Regional Climate Modelling system, for the period 1959–2001. Inter-annual variations in both the observed and downscaled reanalysis gust speeds are presented, with a statistically significant (at the 95% confidence interval) 5% increase across the network in daily maximum gust speeds between 1959 and the early 1990s, followed by an apparent decrease. The benefit of incorporating dynamically downscaled reanalysis data is revealed by the fact that the decrease in gust speeds since 1993 may be placed in the context of a very slight increase displayed over the longer 1959–2001 period. Furthermore, the severity of individual windstorm events is considered, with high profile recent events placed into the context of the long term record. A daily cycle is identified from the station observations in the timing of the daily maximum gust speeds, with an afternoon peak occurring between 12:00–15:00, exhibiting spatial and intra-annual variations.

Calculating distributed glacier mass balance for the Swiss Alps from regional climate model output: A methodical description and interpretation of the results

This study aims at giving a methodical description of the use of gridded output from a regional climate model (RCM) for the calculation of glacier mass balance distribution for the perimeter of the Swiss Alps. The mass balance model runs at daily steps and 100 m spatial resolution, while the regional model (REMO) RCM provides daily grids (∼18 km resolution) of dynamically downscaled reanalysis data. A combination of interpolation techniques and simple subgrid parameterizations is applied to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation, and precipitation. Interpolation schemes are a key element and thus we test different interpolators. For validation, computed mass balances are compared to stake measurements and time series (1979–2003) of observed mass balance. The meteorological input fields are compared to measurements at weather stations. The applied inverse distance weighting introduces systematic biases due to spatial autocorrelation, whereas thin plate splines preserve the characteristics of the RCM output. While summer melt at point locations on several glaciers is well reproduced by the model, accumulation is mostly underestimated. These systematic shifts are correlated to biases of the meteorological input fields. Time series of mass balance obtained from the model run agree well with observed time series. We conclude that the gap in spatial resolution is not a major drawback, given that interpolators and parameterizations are selected upon detailed considerations. Biases in RCM precipitation are a major source for the observed underestimations in mass balance and have to be corrected prior to operational use of the presented approach.