Revisiting Hydrometeorology Using Cloud and Climate Observations

2017 ◽  
Vol 18 (4) ◽  
pp. 939-955 ◽  
Author(s):  
Alan K. Betts ◽  
Ahmed B. Tawfik ◽  
Raymond L. Desjardins
Keyword(s):  
Climate Models ◽  
Potential Temperature ◽  
Warm Season ◽  
Monthly Precipitation ◽  
Mixing Ratio ◽  
Climate Data ◽  
Canadian Prairie ◽  
Near Surface ◽  
Mean Temperature ◽  
Precipitation Anomalies

Abstract This paper uses 620 station years of hourly Canadian Prairie climate data to analyze the coupling of monthly near-surface climate with opaque cloud, a surrogate for radiation, and precipitation anomalies. While the cloud–climate coupling is strong, precipitation anomalies impact monthly climate for as long as 5 months. The April climate has memory of precipitation anomalies back to freeze-up in November, mostly stored in the snowpack. The summer climate has memory of precipitation anomalies back to the beginning of snowmelt in March. In the warm season, mean temperature is strongly correlated to opaque cloud anomalies, but only weakly to precipitation anomalies. Mixing ratio anomalies are correlated to precipitation, but only weakly to cloud. The diurnal cycle of mixing ratio shifts upward with increasing precipitation anomalies. Positive precipitation anomalies are coupled to a lower afternoon lifting condensation level and a higher afternoon equivalent potential temperature; both favor increased convection and precipitation. Regression coefficients on precipitation increase from wet to dry conditions. This is consistent with increased uptake of soil water when monthly precipitation is low, until drought conditions are reached, and also consistent with gravity satellite observations. Regression analysis shows monthly opaque cloud cover is tightly correlated to three climate variables that are routinely observed: diurnal temperature range, mean temperature, and mean relative humidity. The set of correlation coefficients, derived from cloud and climate observations, could be used to evaluate the representation of the land–cloud–atmosphere system in both forecast and climate models.

scholarly journals Understanding Land-Atmosphere-Climate Coupling from the Canadian Prairie Dataset

2018 ◽  
Author(s):  
Alan K Betts ◽  
Raymond L Desjardins
Keyword(s):  
Snow Cover ◽  
Diurnal Cycle ◽  
Radiative Forcing ◽  
Potential Temperature ◽  
Warm Season ◽  
Growing Season ◽  
Cold Season ◽  
Maximum Temperature ◽  
Canadian Prairie ◽  
Cloud Forcing

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land-atmosphere-cloud coupling. The key reason is that trained observers made hourly estimates of opaque cloud fraction that obscures the sun, moon or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality that they can be calibrated against Baseline Surface Radiation Network data to give the climatology of the daily short-wave, longwave and total cloud forcing (SWCF, LWCF and CF). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing reverses sign from negative in the warm season to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10°C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature and the pressure height of the lifting condensation level are all tightly coupled to opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between cloud forcing and the warm season imbalance of the diurnal cycle; which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forci, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of mixing ratio which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season and its dependence on reflective cloud.

scholarly journals Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Environments ◽  
2018 ◽  
Vol 5 (12) ◽  
pp. 129 ◽  
Author(s):  
Alan Betts ◽  
Raymond Desjardins
Keyword(s):  
Snow Cover ◽  
Diurnal Cycle ◽  
Radiative Forcing ◽  
Potential Temperature ◽  
Warm Season ◽  
Growing Season ◽  
Cold Season ◽  
Maximum Temperature ◽  
Canadian Prairie ◽  
Cloud Forcing

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.

scholarly journals Assessment of Local Climate Change: Historical Trends and RCM Multi-Model Projections Over the Salento Area (Italy)

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 978 ◽  
Author(s):  
Marco D’Oria ◽  
Maria Tanda ◽  
Valeria Todaro
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Warm Season ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Local Climate

This study provides an up-to-date analysis of climate change over the Salento area (southeast Italy) using both historical data and multi-model projections of Regional Climate Models (RCMs). The accumulated anomalies of monthly precipitation and temperature records were analyzed and the trends in the climate variables were identified and quantified for two historical periods. The precipitation trends are in almost all cases not significant while the temperature shows statistically significant increasing tendencies especially in summer. A clear changing point around the 80s and at the end of the 90s was identified by the accumulated anomalies of the minimum and maximum temperature, respectively. The gradual increase of the temperature over the area is confirmed by the climate model projections, at short—(2016–2035), medium—(2046–2065) and long-term (2081–2100), provided by an ensemble of 13 RCMs, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). All the models agree that the mean temperature will rise over this century, with the highest increases in the warm season. The total annual rainfall is not expected to significantly vary in the future although systematic changes are present in some months: a decrease in April and July and an increase in November. The daily temperature projections of the RCMs were used to identify potential variations in the characteristics of the heat waves; an increase of their frequency is expected over this century.

scholarly journals Influence of Low-Level Thermodynamic Structure on the Downdraft Properties of Simulated Supercells

2012 ◽  
Vol 140 (8) ◽  
pp. 2575-2589 ◽  
Author(s):  
Jason Naylor ◽  
Mark A. Askelson ◽  
Matthew S. Gilmore
Keyword(s):  
Potential Temperature ◽  
Weather Research And Forecasting ◽  
Cold Pool ◽  
Forecasting Model ◽  
Mixing Ratio ◽  
Near Surface ◽  
Thermodynamic Structure ◽  
Low Level ◽  
Capping Inversion

Abstract Idealized simulations using the Weather Research and Forecasting Model (WRF) were performed to examine the role of capping inversions on the near-surface thermodynamic structure of outflow from simulated supercells. Two simulations were performed: one with the traditional noncapped Weisman and Klemp (WK) analytic sounding and the second with a WK sounding modified to contain a capping inversion. Both sounding environments favor splitting storms and a right-moving supercell by 90 min into the simulation. These two supercell simulations evolve in a qualitatively similar fashion, with both storms exhibiting large, quasi-steady updrafts, hook-shaped appendages in the precipitation mixing ratio field, and prominent localized downdrafts. Results show that the supercell simulated in the capped environment has a surface cold pool with larger values of pseudoequivalent potential temperature (θep) than the cold pool of the supercell produced in the noncapped simulation. Parcels in the surface cold pool of the supercell produced in the capped sounding simulation have a lower origin height than those in the surface cold pool of the supercell produced in the noncapped simulation for all times. Although θep values in the surface cold pool are primarily associated with the origin height of downdraft parcels and the environmental θep at that level, it is shown that nonconservation of θep primarily associated with hydrometeor melting can decrease θep values of downdraft parcels as they descend by several degrees.

Empirical Downscaling of High-Resolution Regional Precipitation from Large-Scale Reanalysis Fields

2012 ◽  
Vol 51 (1) ◽  
pp. 100-114 ◽  
Author(s):  
Robert E. Nicholas ◽  
David S. Battisti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Jiangxi Province ◽  
Monthly Precipitation ◽  
Upper Colorado River Basin ◽  
Precipitation Anomalies ◽  
Empirical Downscaling ◽  
High Skill

AbstractThis study describes an EOF-based technique for statistical downscaling of high-spatial-resolution monthly-mean precipitation from large-scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely separated locations: the southeastern United States, the upper Colorado River basin, China’s Jiangxi Province, and central Europe. For each location, the EOF-based downscaling models successfully reproduce the observed annual cycle while eliminating the biases seen in NCEP–NCAR reanalysis precipitation. They also frequently reproduce the monthly precipitation anomalies with greater fidelity than is seen in the precipitation field derived directly from reanalysis, and they outperform a suite of regional climate models over the two U.S. locations. With the relatively high skill achieved over a range of climate regimes, this technique may be a viable alternative to numerical downscaling of monthly-mean precipitation for many locations.

scholarly journals ERA5-Land: A state-of-the-art global reanalysis dataset for land applications

2021 ◽  
Author(s):  
Joaquín Muñoz-Sabater ◽  
Emanuel Dutra ◽  
Anna Agustí-Panareda ◽  
Clément Albergel ◽  
Gabriele Arduini ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
State Of The Art ◽  
Weather Prediction ◽  
Added Value ◽  
Surface Model ◽  
Climate Data ◽  
Near Surface

Abstract. Framed within the Copernicus Climate Change Service of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the 5th generation of European ReAnalysis (ERA5), hereafter named as ERA5-Land. Once completed, the period covered will span from 1950 to present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, enabling the characterisation of trends and anomalies. This is achieved through global high resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parametrizations that guarantees the use of the state-of-the-art land surface modeling applied to Numerical Weather Prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed behaviour when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available extends from January 1981 to near present, with 2 to 3 months delay with respect to real-time. The segment prior to 1981 is in production, aiming to a release of the whole dataset in summer 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialise NWP and climate models, and to support diverse applications dealing with water resource, land and environmental management. The full ERA5-Land hourly and monthly averaged dataset presented in this paper are available through the Climate Data Store, https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

scholarly journals Independent validation of downscaled climate estimates from a coastal Alaska watershed using local historical weather journals

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12055
Author(s):  
Emily R. Williamson ◽  
Christopher J. Sergeant
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Empirical Data ◽  
Climate Models ◽  
Regional Climate ◽  
Data Sources ◽  
Mean Annual Precipitation ◽  
Monthly Precipitation ◽  
Grid Cells ◽  
Climate Data

Downscaling coarse global and regional climate models allows researchers to access weather and climate data at finer temporal and spatial resolution, but there remains a need to compare these models with empirical data sources to assess model accuracy. Here, we validate a widely used software for generating North American downscaled climate data, ClimateNA, with a novel empirical data source, 20th century weather journals kept by Admiralty Island, Alaska homesteader, Allen Hasselborg. Using Hasselborg’s journals, we calculated monthly precipitation and monthly mean of the maximum daily air temperature across the years 1926 to 1954 and compared these to ClimateNA data generated from the Hasselborg homestead location and adjacent areas. To demonstrate the utility and potential implications of this validation for other disciplines such as hydrology, we used an established regression equation to generate time series of 95% low duration flow estimates for the month of August using mean annual precipitation from ClimateNA predictions and Hasselborg data. Across 279 months, we found strong correlation between modeled and observed measurements of monthly precipitation (ρ = 0.74) and monthly mean of the maximum daily air temperature (ρ = 0.98). Monthly precipitation residuals (calculated as ClimateNA data - Hasselborg data) generally demonstrated heteroscedasticity around zero, but a negative trend in residual values starting during the last decade of observations may have been due to a shift to the cold-phase Pacific Decadal Oscillation. Air temperature residuals demonstrated a consistent but small positive bias, with ClimateNA tending to overestimate air temperature relative to Hasselborg’s journals. The degree of correlation between weather patterns observed at the Hasselborg homestead site and ClimateNA data extracted from spatial grid cells across the region varied by wet and dry climate years. Monthly precipitation from both data sources tended to be more similar across a larger area during wet years (mean ρ across grid cells = 0.73) compared to dry years (mean ρ across grid cells = 0.65). The time series of annual 95% low duration flow estimates for the month of August generated using ClimateNA and Hasselborg data were moderately correlated (ρ = 0.55). Our analysis supports previous research in other regions which also found ClimateNA to be a robust source for past climate data estimates.

scholarly journals European climate change at global mean temperature increases of 1.5 and 2 °C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models

2018 ◽  
Vol 9 (2) ◽  
pp. 459-478 ◽  
Author(s):  
Erik Kjellström ◽  
Grigory Nikulin ◽  
Gustav Strandberg ◽  
Ole Bøssing Christensen ◽  
Daniela Jacob ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Climate Models ◽  
Regional Climate ◽  
Surface Wind ◽  
Near Surface ◽  
The North ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract. We investigate European regional climate change for time periods when the global mean temperature has increased by 1.5 and 2 °C compared to pre-industrial conditions. Results are based on regional downscaling of transient climate change simulations for the 21st century with global climate models (GCMs) from the fifth-phase Coupled Model Intercomparison Project (CMIP5). We use an ensemble of EURO-CORDEX high-resolution regional climate model (RCM) simulations undertaken at a computational grid of 12.5 km horizontal resolution covering Europe. The ensemble consists of a range of RCMs that have been used for downscaling different GCMs under the RCP8.5 forcing scenario. The results indicate considerable near-surface warming already at the lower 1.5 °C of warming. Regional warming exceeds that of the global mean in most parts of Europe, being the strongest in the northernmost parts of Europe in winter and in the southernmost parts of Europe together with parts of Scandinavia in summer. Changes in precipitation, which are less robust than the ones in temperature, include increases in the north and decreases in the south with a borderline that migrates from a northerly position in summer to a southerly one in winter. Some of these changes are already seen at 1.5 °C of warming but are larger and more robust at 2 °C. Changes in near-surface wind speed are associated with a large spread among individual ensemble members at both warming levels. Relatively large areas over the North Atlantic and some parts of the continent show decreasing wind speed while some ocean areas in the far north show increasing wind speed. The changes in temperature, precipitation and wind speed are shown to be modified by changes in mean sea level pressure, indicating a strong relationship with the large-scale circulation and its internal variability on decade-long timescales. By comparing to a larger ensemble of CMIP5 GCMs we find that the RCMs can alter the results, leading either to attenuation or amplification of the climate change signal in the underlying GCMs. We find that the RCMs tend to produce less warming and more precipitation (or less drying) in many areas in both winter and summer.

PBL climatology using IGRA radiosounding data in Mediterranean Basin

2020 ◽  
Author(s):  
Donato Summa ◽  
Fabio Madonna ◽  
Emanuele Tramutola ◽  
Fabrizio Marra ◽  
Benedetto De Rosa ◽  
...  
Keyword(s):  
Mediterranean Basin ◽  
Climate Models ◽  
Potential Temperature ◽  
Vertical Gradient ◽  
Radio Station ◽  
Climate Data ◽  
Free Atmosphere ◽  
Recent Climate ◽  
The Mediterranean ◽  
The Mediterranean Basin

<p>The planetary boundary layer height (PBL) is a critical variable in many applications such as NWP, air quality and climate models. The study of the PBL involves several process and parameters: exchange of momentum, heat, water vapour and tracers from the surface to the free atmosphere therefore,  PBL representation in numerical  models is difficult to achieve and observation are used to improve the quality of the implemented parameterizations.</p><p>This presentation will illustrate a climatology of the height of the PBL and its trend since 1978 to present at different in the Mediterranean Basin.</p><p>The height of the PBL is calculated using the maximum vertical gradient of potential temperature  (θ) obtained from radio Station belonging to the IGRA (Integrated Global Radiosonde) archive related in the Europe Region) and to GRUAN network (GCOS Reference Upper Air Network).  </p><p>The IGRA consists of quality-controlled radiosonde observations of temperature, humidity, and wind at stations across all continents. The earliest year of data is 1905, and the data are updated on a daily basis. Record length, vertical extent and resolution, and availability of variables varies among stations and over time. The GRUAN is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN measurements are providing long-term, high-quality climate data records from the surface, through the troposphere, and into the stratosphere. </p><p>An estimate of uncertainty will be also discussed and correlated with the recent climate changes at the global scale and in the Mediterranean Basin.</p>

What Is the Representation of the Moisture–Tropopause Relationship in CMIP5 Models?*

2015 ◽  
Vol 28 (12) ◽  
pp. 4877-4889 ◽  
Author(s):  
Yutian Wu ◽  
Olivier Pauluis
Keyword(s):  
Annual Cycle ◽  
Seasonal Cycle ◽  
Climate Models ◽  
Potential Temperature ◽  
Specific Humidity ◽  
Cmip5 Models ◽  
Equivalent Potential Temperature ◽  
Near Surface ◽  
Equivalent Potential ◽  
Extratropical Tropopause

Abstract A dynamical relationship that connects the extratropical tropopause potential temperature and the near-surface distribution of equivalent potential temperature was proposed in a previous study and was found to work successfully in capturing the annual cycle of the extratropical tropopause in reanalyses. This study extends the diagnosis of the moisture–tropopause relationship to an ensemble of CMIP5 models. It is found that, in general, CMIP5 multimodel averages are able to produce the one-to-one moisture–tropopause relationship. However, a few biases are observed as compared to reanalyses. First of all, “cold biases” are seen at both the upper and lower levels of the troposphere, which are universal for all seasons, both hemispheres, and almost all CMIP5 models. This has been known as the “general coldness of climate models” since 1990 but the mechanisms remain elusive. It is shown that, for Northern Hemisphere annual averages, the upper- and lower-level “cold” biases are, in fact, correlated across CMIP5 models, which supports the dynamical linkage. Second, a large intermodel spread is found and nearly half of the models underestimate the annual cycle of the tropopause potential temperature as compared to that of the near-surface equivalent potential temperature fluctuation. This implies the incapability of the models to propagate the surface seasonal cycle to the upper levels. Finally, while reanalyses exhibit a pronounced asymmetry in tropopause potential temperature between the northern and southern summers, only a few CMIP5 models are able to capture this aspect of the seasonal cycle because of the too dry specific humidity in northern summer.

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