scholarly journals Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

2017 ◽  
Vol 9 (2) ◽  
pp. 471-495 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

scholarly journals Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

2017 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

<p>Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).</p><p>The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.</p><p>During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.</p>

scholarly journals A Practical Approach to Developing Climate Change Scenarios for Water Quality Models

2019 ◽  
Vol 20 (6) ◽  
pp. 1197-1211 ◽  
Author(s):  
Rakesh K. Gelda ◽  
Rajith Mukundan ◽  
Emmet M. Owens ◽  
John T. Abatzoglou
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
New York ◽  
Wind Speed ◽  
Solar Radiation ◽  
Climate Change Scenarios ◽  
Quality Models ◽  
Hourly Data ◽  
Water Quality Models ◽  
The Impact

Abstract Climate model output is often downscaled to grids of moderately high spatial resolution (~4–6-km grid cells). Such projections have been used in numerous hydrological impact assessment studies at watershed scales. However, relatively few studies have been conducted to assess the impact of climate change on the hydrodynamics and water quality in lakes and reservoirs. A potential barrier to such assessments is the need for meteorological variables at subdaily time scales that are downscaled to in situ observations to which lake and reservoir water quality models have been calibrated and validated. In this study, we describe a generalizable procedure that utilizes gridded downscaled data; applies a secondary bias-correction procedure using equidistance quantile mapping to map projections to station-based observations; and implements temporal disaggregation models to generate point-scale hourly air and dewpoint temperature, wind speed, and solar radiation for use in water quality models. The proposed approach is demonstrated for six locations within New York State: four within watersheds of the New York City water supply system and two at nearby National Weather Service stations. Disaggregation models developed using observations reproduced hourly data well at all locations, with Nash–Sutcliffe efficiency greater than 0.9 for air temperature and dewpoint, 0.4–0.6 for wind speed, and 0.7–0.9 for solar radiation.

scholarly journals Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Sopon Iamsirithaworn ◽  
Wannapong Triampo ◽  
Charin Modchang
Keyword(s):  
Relative Humidity ◽  
Moving Average ◽  
Arima Model ◽  
Southern Region ◽  
Climate Variables ◽  
Climate Data ◽  
Average Temperature ◽  
Minimum Relative Humidity ◽  
Average Maximum ◽  
Influenza Transmission

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

Assessment of the future climate potential for tourism over Europe using a combination of downscaling approaches and quantitative impact models

2020 ◽  
Author(s):  
Maria Francisca Cardell ◽  
Arnau Amengual ◽  
Romualdo Romero
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Future Climate ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Model Errors ◽  
Present Climate ◽  
Ensemble Strategy ◽  
The Future

<p>Europe and particularly, the Mediterranean countries, are among the most visited tourist destinations worldwide, while it is also recognized as one of the most sensitive regions to climate change. Climate is a key resource and even a limiting factor for many types of tourism. Owing to climate change, modified patterns of atmospheric variables such as temperature, rainfall, relative humidity, hours of sunshine and wind speed will likely affect the suitability of the European destinations for certain outdoor leisure activities.</p><p>Perspectives on the future of second-generation climate indices for tourism (CIT) that depend on thermal, aesthetic and physical facets are derived using model projected daily atmospheric data and present climate “observations”. Specifically, daily series of 2-m maximum temperature, accumulated precipitation, 2-m relative humidity, mean cloud cover and 10-m wind speed from ERA-5 reanalysis are used to derive the present climate potential. For projections, the same daily variables have been obtained from a set of regional climate models (RCMs) included in the European CORDEX project, considering the rcp8.5 future emissions scenario. The adoption of a multi-model ensemble strategy allows quantifying the uncertainties arising from the model errors and the GCM-derived boundary conditions. To properly derive CITs at local scale, a quantile–quantile adjustment has been applied to the simulated regional scenarios. The method detects changes in the continuous CIT cumulative distribution functions (CDFs) between the recent past and successive time slices of the simulated climate and applies these changes, once calibrated, to the observed CDFs. </p><p>Assessments on the future climate potential for several types of tourist activities in Europe (i.e., sun, sea and sand (3S) tourism, cycling, cultural, football, golf, nautical and hiking) will be presented by applying suitable quantitative indicators of CIT evolutions adapted to regional contexts. It is expected that such kind of information will ultimately benefit the design of mitigation and adaptation strategies of the tourist sector.</p>

POSSIBLE CHANGES OF CLIMATE CONDITIONS IN UKRAINE TO THE MIDDLE OF THE XXI CENTURY

2020 ◽  
pp. 94-100
Author(s):  
L.V. Malytska ◽  
V. O Balabukh
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Climate Model ◽  
Temporal Distribution ◽  
Climatic Parameters ◽  
Average Wind Speed ◽  
Climate Conditions ◽  
A1b Scenario

In Ukraine, as in the world, substantial climatic changes have happened throughout past decades. It is a fact that they are manifested in changing of parameters of the thermal regime, regimes of wind and humidity. It is expected that they will be observed also in future that will lead to aggravation of negative effects and risks due to climate change. That determines the relevance of the problem of forecasting such changes in future both globally and regionally. After all, knowledge of climate’s behavior in future is very important in the development of strategies, program and measures to adapt to climate change. The article is devoted to assessing spatio-temporal distribution main climatic indicators (air temperature, wind speed and relative humidity) in Ukraine, their variability and the probable values to the middle of the 21st century (2021-2050). Projection of changes in meteorological conditions was made for A1B scenario of SRES family using data of the regional climate model REMO and data from the hydrometeorological observation network of Ukraine (175 stations). Estimated data obtained from the European FP-6 ENSEMBLES project with a resolution of 25 km. For spatial distribution (mapping) we used open-source Geographic Information System QGIS, type of geographic coordinate system for project is WGS84. In the middle of the XXI century, if A1B scenario is released, it is expected a significant changes of climatic parameters regarding the 1981-2010 climatic norm: air temperature is rise by 1,5 °C, average wind speed is decrease by 5-8%, relative humidity in winter probably drop by 2%, but in summer it rises by 1,5%. The unidirectionality of the changes is characteristic only of air temperature, for wind speed and relative humidity the changes are in different directions. The intensity of changes is also not uniform across the country for all climatic parameters, has its regional and seasonal features. Statistical likelihood for most of highlighted changes for all climatic parameters is 66 % and more, the air temperature change is virtually certain (p-level <0.001).

scholarly journals Assessment of Spatiotemporal Variability and Trend Analysis of Reference Crop Evapotranspiration for the southern region of Peninsular India

2021 ◽  
Author(s):  
Jayashree Tenkila Ramachandra ◽  
Subba Reddy Nandanavana Veerappa ◽  
Dinesh Acharya Udupi
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Water Resource Management ◽  
Spatiotemporal Variability ◽  
Trend Test ◽  
Accurate Estimation ◽  
Peninsular India ◽  
Climatic Zones ◽  
Statistical Measures

Abstract Accurate estimation of reference evapotranspiration (ET0) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET0 across diverse climate regimes over the past decades. The Python implementation for estimation of daily and monthly ET0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 through 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET0 values was higher than temporal indicating major differentiation of ET0 values was with respect to the stations rather than years under study. The non-parametric Mann-Kendall test conducted at 1% significance level on the annual ET0 values revealed that statistically significant increasing trend was observed for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity and solar radiation signify their influence the annual ET0 values. The magnitude changes in the trends detected by the Theil Sen’s slope indicated that increasing values of mean temperature, solar radiation and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET0 values for the 10 stations. A trivial impact of wind speed on annual ET0 values was observed for the stations. Kalburgi and Udupi stations exhibited positive ET0 trend with the highest and lowest annual values among ten stations.

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