Mean Temperature Bias Research Articles

Hydrological response of Andean catchments to recent glacier mass loss

Abstract. The impacts of the accelerated glacier retreat in recent decades on glacier runoff changes are still unknown in most Andean catchments, increasing uncertainties in estimating water availability. This particularly affects the outer tropics and Dry Andes, heavily impacted by prolonged droughts. Current global estimates overlook climatic and morphometric disparities, which significantly influence model parameters, among Andean glaciers. Meanwhile, local studies have used different approaches to estimate glacier runoff in a few catchments. Improving 21st-century glacier runoff projections relies on calibrating and validating models using corrected historical climate inputs and calibrated parameters across diverse glaciological zones. Here, we simulate glacier evolution and related runoff changes between the periods 2000–2009 and 2010–2019 across 786 Andean catchments (11 282 km2 of glacierized area, 11° N to 55° S) using the Open Global Glacier Model (OGGM). TerraClimate atmospheric variables were corrected using in situ data, getting a mean temperature bias by up to 2.1 °C and enhanced monthly precipitation. Glacier mass balance and volume were calibrated, where melt factor and the Glen A parameter exhibited significant alignment with varying environmental conditions. Simulation outcomes were validated against in situ data in three documented catchments (with a glacierized area > 8 %) and monitored glaciers. Our results at the Andes scale reveal an average reduction of 8.3 % in glacier volume and a decrease of 2.2 % in surface area between the periods 2000–2009 and 2010–2019. Comparing these two periods, glacier and climate variations have led to a 12 % increase in mean annual glacier melt (86.5 m3 s−1) and a decrease in rainfall on glaciers of −2 % (−7.6 m3 s−1) across the Andes, with both variables comprising the glacier runoff. We confirmed the utility of our corrected regional simulations of glacier runoff contribution at the catchment scale, where our estimations align with previous studies (e.g., Maipo 34° S, Chile) as well as provide new insights on the seasonal glaciers' largest contribution (e.g., La Paz 16° S, Bolivia) and new estimates of glacier runoff contribution (e.g., Baker 47° S, Chile).

Quality Assessment of the Atmospheric Radio Occultation Profiles from FY-3E/GNOS-II BDS and GPS Measurements

The Fengyun-3E (FY-3E) satellite carrying the advanced Global Navigation Satellite System (GNSS) Radio Occultation Sounder-II (GNOS-II) is already in operation for radio occultation (RO) observation, with the BeiDou Navigation Satellite System (BDS-2 and BDS-3) and Global Positioning System (GPS) signals tracking capability. FY-3E BDS and GPS RO signals tracking capability were first evaluated by comparing their penetration depths, and then the quality of the refractivity, temperature, and specific humidity profiles was analyzed with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data. Results show the mean penetration depth of BDS occultations was 1.65 km compared to 1.09 km of GPS occultations. Between 5 and 25 km, the mean refractivity bias of the BDS (GPS) was −0.14% (0.01%) with the mean standard deviation (SD) being 1.11% (1.52%); the mean temperature biases of both were within ±0.1 K, and the mean SD of BDS was 1.1 K compared to 1.2 K for the GPS; BDS/GPS specific humidity bias was within ±0.3 g/kg with corresponding SD being less than 1.3 g/kg. Seasonal deviations of specific humidities were largest in summer and smallest in winter. Latitudinal deviations over the tropics were generally higher than in other areas. Enriched quantity and high accuracy and precision after careful calibration will promote the FY-3E RO profiles as a reliable data source for the RO community.

Clumped and oxygen isotope sclerochronology methods tested in the bivalve Lucina pensylvanica

Geochemical signatures preserved within the geologic record can be used to reconstruct past mean temperature and seasonality, but in order to accurately apply any geochemical proxy method in the past, a rigorous study of how the recorded proxy is related to temperature in the modern setting must be conducted. Here, we assess the ability of multiple isotope techniques to correctly record mean annual temperature and seasonality in the bivalve Lucina pensylvanica. We compare subannual-resolution δ18O-based, seasonally-targeted and continuous high-resolution (H.R.) clumped isotope (Δ47)-based thermometry methods, as well as multiple data treatment methods for each, to determine which approach best matches known modern temperatures (maximum and minimum absolute temperature and annual temperature range), with the goal of defining the ideal sampling scheme for use on fossil shells. In L. pensylvanica shells collected from 7 sites, we observe neither mean temperature nor seasonal biases. Mean annual temperature is best matched by averaging all seasonally-targeted Δ47-temperatures. Seasonality is best matched by averaging δ18Ocarb-based temperatures from all summers and all winters before taking the difference. Of two data treatment approaches applied to the continuous high-resolution Δ47-based temperatures, “data optimization” is apparently better at resolving smaller seasonal temperature differences. In contrast, “data smoothing” produces a temperature record unbiased by prior assignment of seasonal extremes and has the simultaneous ability to detect subannual variability in δ18Ow. However, accurate application of H.R. Δ47 methods must balance sampling resolution and growth rate. If sampling resolution is high enough relative to the growth rate (∼1 pt./month or better), we recommend continuous high-resolution Δ47-thermometry with data smoothing. If this resolution cannot be achieved due to slow growth rates or insufficient shell size, we recommend pairing subannual δ18Ocarb-based and seasonally-targeted Δ47-based temperature reconstruction to acquire seasonal range in temperature and absolute temperature extremes.

The Performance of the ERA‐Interim and ERA5 Atmospheric Reanalyses Over Weddell Sea Pack Ice

AbstractWe use meteorological measurements from three drifting buoys to evaluate the performance of the ERA‐Interim and ERA5 atmospheric reanalyses from the European Center for Medium‐Range Weather Forecasts over the Weddell Sea ice zone. The temporal variability in surface pressure and near‐surface air temperature is captured well by the two reanalyses but both reanalyses exhibit a warm bias relative to the buoy measurements. This bias is small at temperatures close to 0°C but reaches 5–10°C at −40°C. For two of the buoys the mean temperature bias in ERA5 is significantly smaller than that in ERA‐Interim while for the third buoy the biases in the two products are comparable. 10 m wind speed biases in both reanalyses are small and may largely result from measurement errors associated with icing of the buoy anemometers. The biases in downwelling shortwave and longwave radiation are significant in both reanalyses but we caution that the pattern of bias is consistent with potential errors in the buoy measurements, caused by accumulation of snow and ice on the radiometers. Overall, our study suggests that, with the exception of near‐surface temperature, both reanalyses reproduce the buoy measurements to within the limits of measurement uncertainty. We suggest that the significant biases in near‐surface air temperature may result from the simplified representation of sea ice used in the reanalysis models, and we recommend the use of a more sophisticated representation of sea ice, including variable ice and snow thicknesses, in future reanalyses.

Continuous temperature soundings at the stratosphere and lower mesosphere with a ground-based radiometer considering the Zeeman effect

Abstract. Continuous temperature observations at the stratosphere and lower mesosphere are rare. Radiometry opens the possibility of observing microwave emissions from two oxygen lines to retrieve temperature profiles at all altitudes. In this study, we present observations performed with a temperature radiometer (TEMPERA) at the MeteoSwiss station at Payerne for the period from 2014 to 2017. We reanalyzed these observations with a recently developed and improved retrieval algorithm accounting for the Zeeman line splitting in the line center of both oxygen emission lines at 52.5424 and 53.0669 GHz. The new temperature retrievals were validated against MERRA2 reanalysis and the meteorological analysis NAVGEM-HA. The comparison confirmed that the new algorithm yields an increased measurement response up to an altitude of 53–55 km, which extends the altitude coverage by 8–10 km compared to previous retrievals without the Zeeman effect. Furthermore, we found correlation coefficients comparing the TEMPERA temperatures with MERRA2 and NAVGEM-HA for monthly mean profiles to be in the range of 0.8–0.96. In addition, mean temperature biases of 1 and −2 K were found between TEMPERA and both models (MERRA2 and NAVGEM-HA), respectively. We also identified systematic altitude-dependent cold and warm biases compared to both model data sets.

Simulating the impact of extended desertification on Indian hydro climate using ICTP-RegCM4.4.5.10 model

Presented here is the numerical study of impact of extended desertification of the Thar desert (a data scarce arid region), on the Indian Summer Monsoon (ISM) meteorology using a robust state-of-the-art ICTP - regional climate model (RCM) RegCMv4.4.5.10 coupled with Biosphere – Atmosphere Transfer Scheme (BATS) land surface scheme. Firstly, the evolution of ISM precipitation, mean temperature and soil moisture during June to September 2001–2010 is verified using Emanuel over land and Grell over ocean with Arakawa and Schubert (1974) closure, in combination with the two planetary boundary layer (PBL) parameterization schemes, Holtslag and University of Washington - UW Turbulence closure model for PBL. Then the feedback due to extended desertification between land surface and PBL, during ISM regime is studied. The RCM simulations with Emanuel over land and Grell over ocean with Arakawa and Schubert (1974) closure in combination with UW PBL scheme, is able to properly simulate the rainfall, mean temperature JJAS climatology with bias in rainfall ~ 2 mm/day and bias in mean temperature is ~ 0.5–1 °C over the monsoon core region. Numerical experiments (control and design experiment) with the above mentioned combination of PBL schemes is performed for investigating the impact of extended desertification on the changes in the ISM precipitation, surface fluxes, circulation pattern, moisture fluxes and net radiation etc. In the extended desert design experiment, the land-use pattern in the model over Thar Desert (i.e. northwestern part of Indian sub-continent between Pakistan and northwestern India) is changed from “crop/mixed farming and semi-desert” to “desert/semi-desert” type of vegetation, mimicking extended desertification in the RegCMv4.4.5.10 model. Results show that due to increase in desertification over Thar deserts there is increase in albedo resulting from a decrease in plant cover, along with formation of anomalous anti-cyclonic circulation causes a decrease in JJAS rainfall (p < 0.01) over north, north-west India by −1 to −2 mm/day (~25% reduction). Anomalous anti-cyclonic circulation over the Indian land mass in the extended desertification experiment causes less convective heating and is responsible for the decrease in precipitation (with respect to control experiment). The anomalous anti-cyclonic circulation developed covers northern part of Bay of Bengal resulting in decrease in moisture advection of the order of −12 to −18 kgm-1sec-1 (~10% reduction). This drives away all moisture from the nearby regions. The effect of extended desertification induced feedbacks on variability and dominant modes of variability of Indian monsoon precipitation and soil wetness is also studied and reported. Finally, it is concluded that the relative magnitudes of the changes in albedo and vegetation (or vegetation roughness length) are important determinants of the size of climate change due to prescribed extended desert landscape over Thar desert region of Indian subcontinent. The results of this numerical study provides a basis and reference study for LULCC and mitigation measure human beings need to undertake for climate change adaptation.

The impact of time‐varying sea surface temperature on UK regional atmosphere forecasts

AbstractA new approach to improve the ocean surface boundary condition used in regional numerical weather prediction is proposed. Typically, regional atmosphere forecast systems assume a fixed sea surface temperature during a simulation. The study assesses the use of ocean temperature from an operational regional ocean model as an evolving lower boundary in a kilometre‐scale regional atmosphere configuration centred on the UK. Simulations of a winter and two five day duration summer case studies associated with anomalously warm temperatures are considered. The largest impact is found in summer, when a growing cold bias in mean temperature over land compared with observations is apparent when using a fixed global‐scale analysis lower boundary condition. The mean error is improved by 0.1 K when using a fixed temperature boundary condition from a kilometre‐scale regional ocean model initial condition. When using hourly surface temperature data from the same regional ocean model, the error is improved by 0.5 K for this case. Prediction of daytime maximum air temperature is also improved during the summer heat wave cases. A winter case study shows marginal improvement over the ocean and negligible changes over land. These results are confirmed in longer duration experiments using an hourly cycling regional forecast system with data assimilation for summer and winter periods. A systematic and statistically significant improvement of near‐surface temperature verification relative to the observations over land is demonstrated for both summer and winter using the new approach. This study supports future operational implementation of a time‐varying lower boundary for regional numerical weather prediction.

Comparison of temperature and humidity profiles retrieved from INSAT-3DR sounder with high resolution radiosonde measurements

<abstract> We evaluate temperature and humidity profiles retrieved from an INSAT-3DR sounder against radiosonde measurements for one year during 2017–2018. This evaluation is carried out in terms of slope, intercept, bias, Root Mean Square Error (RMSE), and correlation coefficient in temperature and relative humidity profiles. This comparison provided first-hand information about the performance of INSAT-3DR sounder retrievals. This validation exercise is unique in terms of comparing with the high resolution (1 Hz) radiosonde measurements performed in the afternoon time when there is maximum convection driven by solar radiation. 276 pairs of co-located temperatures have been compared. INSAT-3DR temperature retrievals are strongly linearly correlated to radiosonde measurements, with the slope as 1.001 and correlation coefficient of 0.99, while the mean temperature bias between INSAT-3DR and radiosonde is -0.23°C with RMSE of 1.9°C. Comparison of Relative humidity retrieved from INSAT-3DR to the radiosonde data results in RMSE of 9.8% with a slope of 0.99 with a correlation coefficient of 0.77. The collocation of data points for validation is done matching in space [Latitude, Longitude, Pressure level (height)] and time (±30 minutes). The effect of relaxing the space and time window on the validation statistics is also studied for the first time. </abstract>

Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon

AbstractIt is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high‐latitude region where much of the precipitation falls as snow. Leave‐one‐out cross‐validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long‐term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska.

Climatic projections of heating season in Ukraine up to the middle of the 21st century

The paper presents projections for the balanced scenario A1B of specialized climatic indicators of the heating period for the territory of Ukraine for decades until the middle of the 21st century. Prediction of mean temperature, duration and heating degree-days are based on calculations of the previously formed and adapted to Ukraine ensemble of 10 regional climate models from the European project ENSEMBLES. The main and specialized climatic indicators calculated on all nodes of a regular grid of 25×25 km were verified by E-Obs data for the period 2001―2010. A negligible bias in the heating period mean temperature (in average less than 0.01°C) and an acceptable bias in duration (with a mean value of less than 6,4 days or <4 %) were found in the verification that resulted in obtaining an average bias in heating degree-days by about 4 %. Forecasting of specialized indicators of the heating season for future periods was performed with corresponding bias corrections. The spatial and temporal changes of heating period indices for the decades to the middle of the 21st century have been analyzed and averaging values for administrative units of Ukraine have been calculated. Obtained insignificant, within the limits of 0―0,2 °С , increase of the mean heating period temperature for most of the country in the modern decade of 2011―2020 in relation to the base period of 2001―2010, reducing the heating period duration by an average of 3 days and reducing values of heating degree-days by 2―3 %. By the middle of the 21st century the mean heating period temperature will increase significantly with maximum values of 1,5 °С in the northeastern, central and eastern parts of the country, and the least increasing of 0,8 °С to the south, southwest and the Crimea. The heating period duration will be reduced by 15―18 days in average, with a maximum of 20 days to the south. The values of heating degree-days will decrease in most areas by 12―16 %, with maximum by 20 % in the Crimea, and the minimum reduction by 9―12 % in the Carpathians. Results obtained in the study may be used, primarily, for the calculation of heating costs and other needs of the energy sector and other national economy sectors for the overall development of infrastructure and other needs of any settlement in the territory of Ukraine.

Analysis of Monsoon Mission Coupled Forecasting System (MMCFS) model simulations of sub-division scale temperatures over India for the hot weather season (April–June)

This study examines skill of the Monsoon Mission Climate Forecasting System (MMCFS) model simulations of monthly and seasonal maximum, minimum and mean temperatures of hot weather season (April, May and June) for the period 1982–2008 over India. The hindcast skill at the sub-division and all India scales were computed. The hindcasts were prepared using initial conditions (ICs) pertaining to January, February and March. The bias-corrected forecast for the 2016 AMJ season was also verified with the high resolution gridded temperature data of the India Meteorological Department (IMD). Standard verification skill scores, namely correlation coefficient (CC) and root mean square error (RMSE) have been used to assess the hindcast skill at various lead times. The grid point level statistical bias-correction was successful in reducing the bias and RMSE of the MMCFS model hindcast at the sub-division and all India scales. The hindcast analysis showed that for all the considered ICs and during all the months (April, May and June) and AMJ season for maximum, minimum and mean temperature bias of four sub-divisions Jammu & Kashmir (J&K), Himachal Pradesh, Uttarakhand, and Arunachal Pradesh showed bias $${\le }{-}2.0^{\circ }\hbox {C}$$ and four sub-divisions Saurashtra and Kutch, Bihar, Gangetic West Bengal, Sub-Himalayan West Bengal (SHWB) and Sikkim showed bias $${\ge }2^{\circ }\hbox {C}$$ . Hindcast based on the February and March ICs showed the best skill both in sub-division scale as well as in all India scale. Similarly, among the three months of the AMJ season, model skills based on considered ICs were best for the April month. Many of the sub-divisions from northwest India and neighboring central India and along the west coast showed significant hindcast skill for simulations based on February and March ICs. For the all India averaged temperature, March IC based forecast showed the highest skill for all the months and AMJ season followed by February IC. The MMCFS model forecasts for the 2016 monthly and seasonal temperatures were able to indicate correct signs of the observed temperature anomalies in most of the sub-divisions. The pattern anomaly correlations for the May and June forecasts based on March IC were significant at $$\ge $$ 95% level.

Modelling Glaciers in the HARMONIE-AROME NWP model

Abstract. HARMONIE-AROME is a convection-permitting non-hydrostatic model that includes the multi-purpose SURFEX surface model. It is developed for high resolution (1–3 km) weather forecasting and applied in a number of regions in Europe and the Mediterranean. A version of HARMONIE-AROME is also under development for regional climate modelling. Here we run HARMONIE-AROME for a domain over Greenland that includes a significant portion of the Greenland ice sheet. The model output reproduces temperature, wind speed and direction and relative humidity over the ice sheet well when compared with the observations from PROMICE automatic weather stations (AWS) operated within the model domain on the ice sheet (mean temperature bias 1.31 ± 3.6 K) but we identified a much lower bias (−0.16 ± 2.3 K) at PROMICE sites on days where melt does not occur at the ice sheet surface and is thus an artefact of the simplified surface scheme over glaciers in the existing HARMONIE-AROME operational set-up. The bias in summer time temperature also affects wind speed and direction as the dominant katabatic winds are caused by the cold ice surface and slope gradient. By setting an upper threshold to the surface temperature of the ice surface within SURFEX we show that the weather forecast error over the Greenland ice sheet can be reduced in summer when glacier ice is exposed. This improvement will facilitate accurate ice melt and run-off computations, important both for ice surface mass budget estimation and for commercial applications such as hydro-power forecasting. Furthermore, the HCLIM regional climate model derived from HARMONIE-AROME will need to accurately account for glacier surface processes in these regions in order to be used to accurately compute the surface mass budget of ice sheets and glaciers, a key goal of regional climate modelling studies in Greenland.

Equatorial Atlantic interannual variability and its relation to dynamic and thermodynamic processes

Abstract. The contributions of the dynamic and thermodynamic forcing to the interannual variability of the equatorial Atlantic sea surface temperature (SST) are investigated using a set of interannual regional simulations of the tropical Atlantic Ocean. The ocean model is forced with an interactive atmospheric boundary layer, avoiding damping toward prescribed air temperature as is usually the case in forced ocean models. The model successfully reproduces a large fraction (R2 = 0.55) of the observed interannual variability in the equatorial Atlantic. In agreement with leading theories, our results confirm that the interannual variations of the dynamical forcing largely contributes to this variability. We show that mean and seasonal upper ocean temperature biases, commonly found in fully coupled models, strongly favor an unrealistic thermodynamic control of the equatorial Atlantic interannual variability.

A comprehensive approach for the simulation of the Urban Heat Island effect with the WRF/SLUCM modeling system: The case of Athens (Greece)

This study presents a comprehensive modeling approach for simulating the spatiotemporal distribution of urban air temperatures with a modeling system that includes the Weather Research and Forecasting (WRF) model and the Single-Layer Urban Canopy Model (SLUCM) with a modified treatment of the impervious surface temperature. The model was applied to simulate a 3-day summer heat wave event over the city of Athens, Greece. The simulation, using default SLUCM parameters, is capable of capturing the observed diurnal variation of urban temperatures and the Urban Heat Island (UHI) in the greater Athens Area (GAA), albeit with systematic biases that are prominent during nighttime hours. These biases are particularly evident over low-intensity residential areas, and they are associated with the surface and urban canopy properties representing the urban environment. A series of sensitivity simulations unravels the importance of the sub-grid urban fraction parameter, surface albedo, and street canyon geometry in the overall causation and development of the UHI effect. The sensitivities are then used to determine optimal values of the street canyon geometry, which reproduces the observed temperatures throughout the simulation domain. The optimal parameters, apart from considerably improving model performance (reductions in mean temperature bias from 0.30°C to 1.58°C), are also consistent with actual city building characteristics – which gives confidence that the model set-up is robust, and can be used to study the UHI in the GAA in the anticipated warmer conditions in the future.

Analysis of near‐surface biases in ERA‐Interim over the Canadian Prairies

AbstractWe quantify the biases in the diurnal cycle of temperature in ERA‐Interim for both warm and cold season using hourly climate station data for four stations in Saskatchewan from 1979 to 2006. The warm season biases increase as opaque cloud cover decreases, and change substantially from April to October. The bias in mean temperature increases almost monotonically from small negative values in April to small positive values in the fall. Under clear skies, the bias in maximum temperature is of the order of −1°C in June and July, and −2°C in spring and fall; while the bias in minimum temperature increases almost monotonically from +1°C in spring to +2.5°C in October. The bias in the diurnal temperature range falls under clear skies from −2.5°C in spring to −5°C in fall. The cold season biases with surface snow have a different structure. The biases in maximum, mean and minimum temperature with a stable BL reach +1°C, +2.6°C and +3°C respectively in January under clear skies. The cold season bias in diurnal range increases from about −1.8°C in the fall to positive values in March. These diurnal biases in 2 m temperature and their seasonal trends are consistent with a high bias in both the diurnal and seasonal amplitude of the model ground heat flux, and a warm season daytime bias resulting from the model fixed leaf area index. Our results can be used as bias corrections in agricultural modeling that use these reanalysis data, and also as a framework for understanding model biases.

Future changes due to model biases in probabilities of extreme temperatures over East Asia using CMIP5 data

ABSTRACTThis study examines the performances of 31 global climate models in the Coupled Model Inter‐comparison Project 5 (CMIP5) in terms of probability density functions (PDFs) for maximum (Tmax) and minimum (Tmin) air temperatures over East Asia in the present and CMIP5‐model projected future changes. In general, most of models well reproduce warm‐season peak for both Tmax and Tmin but exhibit large inter‐model spread for simulating cold‐season peak, especially for Tmin. Minimum values of Tmin and Tmax are more strongly dependent upon model selection than maximum values of them. For the last 25 years of the 21st century, under the Representative Concentration Pathways 4.5 scenario, models project shifts toward warmer values in the PDFs of Tmax and Tmin and broadening in the shape of PDFs. Models with warm biases in PDFs tend to show larger shifts in temperature changes, but seasonal mean temperature biases do not affect to future changes. It is notable that the broadening of PDFs in the future influences temperature extreme events. Using the changes in probabilities of heat waves as one of extreme temperature events by comparing multi‐model ensemble (MME) and models with good performance of PDFs, this study shows that MME tends to overestimate its duration. Our findings suggest that future changes in temperature extremes projected by models are strongly come from the biases detected in those models when simulating present extreme temperature PDFs. Therefore, correcting the intrinsic biases of models rather than seasonal mean correction is necessary to reduce the uncertainties in predicting future changes in temperature extremes.

Association between mean and interannual equatorial Indian Ocean subsurface temperature bias in a coupled model

In the present study the association between mean and interannual subsurface temperature bias over the equatorial Indian Ocean (EIO) is investigated during boreal summer (June through September; JJAS) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. Anomalously high subsurface warm bias (greater than 3 °C) over the eastern EIO (EEIO) region is noted in CFSv2 during summer, which is higher compared to other parts of the tropical Indian Ocean. Prominent eastward current bias in the upper 100 m over the EIO region induced by anomalous westerly winds is primarily responsible for subsurface temperature bias. The eastward currents transport warm water to the EEIO and is pushed down to subsurface due to downwelling. Thus biases in both horizontal and vertical currents over the EIO region support subsurface warm bias. The evolution of systematic subsurface warm bias in the model shows strong interannual variability. These maximum subsurface warming episodes over the EEIO are mainly associated with La Nina like forcing. Strong convergence of low level winds over the EEIO and Maritime continent enhanced the westerly wind bias over the EIO during maximum warming years. This low level convergence of wind is induced by the bias in the gradient in the mean sea level pressure with positive bias over western EIO and negative bias over EEIO and parts of western Pacific. Consequently, changes in the atmospheric circulation associated with La Nina like conditions affected the ocean dynamics by modulating the current bias thereby enhancing the subsurface warm bias over the EEIO. It is identified that EEIO subsurface warming is stronger when La Nina co-occurred with negative Indian Ocean Dipole events as compared to La Nina only years in the model. Ocean general circulation model (OGCM) experiments forced with CFSv2 winds clearly support our hypothesis that ocean dynamics influenced by westerly winds bias is primarily responsible for the strong subsurface warm bias over the EEIO. This study advocates the importance of understanding the ability of the models in representing the large scale air–sea interactions over the tropics and their impact on ocean biases for better monsoon forecast.

Evaluation of the COSMO‐CLM high‐resolution climate simulations over West Africa

AbstractThe evaluation of a high‐resolution simulation at 0.11° (12 km) with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, applied over West Africa, is presented. This simulation is nested in a CCLM run at resolution of 0.44°, driven with boundary forcing data from the ERA‐Interim reanalysis, and covers the period from 1981 to 2010. The simulated temperature and precipitation are evaluated using three selected climate indices for temperature and eight indices for precipitation in five different regions against a new, daily precipitation climatology covering West Africa and against other state of the art climatologies. The obtained results indicate that CCLM is able to reproduce the observed major climate characteristics including the West African Monsoon within the range of comparable regional climate modeling evaluations studies, but substantial uncertainties remain, especially in the Sahel zone. The seasonal mean temperature bias for the rainy season from June to September ranges from −0.8°C to −1.1°C. The CCLM simulations also underestimate the observed precipitation with biases in precipitation reaching −10% in the high‐resolution and −20% in the low‐resolution model runs. CCLM extends the monsoon precipitation belt too far north, which results in an overestimation of precipitation in the Sahel zone of up to 60%. In the coastal zone, the precipitation is underestimated by up to −90%. These biases in precipitation amounts are associated with errors in the precipitation seasonality. The added value of the higher resolution of the nested run is reflected in a smaller bias in extreme precipitation statistics with respect to the reference data.

EURO‐CORDEX regional climate model analysis for the Greater Alpine Region: Performance and expected future change

AbstractSimulations from 13 highly resolved regional climate models run within the Coordinated Downscaling Experiment initiative at 0.11° resolution with boundary forcings from five different Coupled Model Intercomparison Project Phase 5 global models are employed to derive future climate change signal for the Greater Alpine Region (GAR) and four smaller investigation areas. Evaluation statistics include mean temperature and precipitation, frequency of days with precipitation over 1 mm and over 15 mm, 90% quantile of the frequency distribution, and maximum number of consecutive dry days. The evaluation for the period from 1971 to 2000 indicates that the models reproduce spatial seasonal precipitation patterns. In general, the simulations underestimate the seasonal mean temperature and overestimate the mean precipitation values. In GAR the ensemble seasonal mean temperature bias ranges from −0.8 to −1.9°C. The bias in precipitation varies between +14.8% in summer and +41.6% in the winter season. Larger errors are found for other statistics and in the investigated regions. In general, no significant gains in the quality of reproduction of the observed precipitation and temperature statistics compared to previous experiments can be identified. The temperature calculations for 2071–2100 related to the period from 1971 to 2000 in the GAR area show ensemble mean increases in the seasonal mean 2 m temperature of 2.5°C in fall and winter, 2.4°C in summer, and 1.9°C in spring. In the same area, precipitation is simulated to increase up to 12.3% in winter and 5.7% in spring. Only minor changes of the ensemble mean are predicted with +2.3% in fall and −1.7% in summer.

Underestimated warming of northern Canada in the Berkeley Earth temperature product

ABSTRACTThe Berkeley Earth surface temperature (BETP) project provides gridded global temperature anomaly products using an automated geostatistical approach to adjust station data for systematic biases. Despite its widespread usage, the BETP data set has not been evaluated at the national‐scale, especially in data‐sparse high latitude environments. This study provides an evaluation of the BETP product across all of Canada using 333 climate stations made available from the homogenized Environment Canada station network (HTcan). Comparison between co‐located monthly air temperature anomalies for the two data sets suggests small differences between the two products for mean surface (∼2 m) air temperature. However, the relatively minimal bias in mean temperature is a consequence of contrasting cold and warm biases in minimum and maximum air temperatures, respectively, that are larger but effectively even out when averaged together. The BETP product is shown to exhibit systematic underestimation of recent regional warming in northern Canada which when combined with an overestimation of warmth earlier in the record results in an observable reduction in warming rates for minimum and mean temperature anomalies since 1950. The temporal evolution and spatial pattern of the observed biases suggest that the BETP‐automated adjustments to station data in northern Canada miss some inhomogeneities in the raw station data. These results highlight the need for enhanced data recovery and homogenization efforts in data‐sparse high latitude regions and emphasize the importance of national‐scale climate data sets for evaluating global gridded products. We also recommend caution when using the BETP minimum and maximum monthly air temperature products for long‐term trend analyses.