Bias‐corrections on aridity index simulations of climate models by observational constraints

2021 ◽  
Author(s):  
Haipeng Yu ◽  
Qiang Zhang ◽  
Yun Wei ◽  
Chenxi Liu ◽  
Yu Ren ◽  
...  
Keyword(s):  
Climate Models ◽  
Aridity Index ◽  
Observational Constraints ◽  
Bias Corrections

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

scholarly journals Terrestrial Aridity and Its Response to Greenhouse Warming across CMIP5 Climate Models

2015 ◽  
Vol 28 (14) ◽  
pp. 5583-5600 ◽  
Author(s):  
Jacob Scheff ◽  
Dargan M. W. Frierson
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Aridity Index ◽  
Global Climate Models ◽  
Greenhouse Warming ◽  
Dimensionless Ratio ◽  
The Tropics

Abstract The aridity of a terrestrial climate is often quantified using the dimensionless ratio of annual precipitation (P) to annual potential evapotranspiration (PET). In this study, the climatological patterns and greenhouse warming responses of terrestrial P, Penman–Monteith PET, and are compared among 16 modern global climate models. The large-scale climatological values and implied biome types often disagree widely among models, with large systematic differences from observational estimates. In addition, the PET climatologies often differ by several tens of percent when computed using monthly versus 3-hourly inputs. With greenhouse warming, land P does not systematically increase or decrease, except at high latitudes. Therefore, because of moderate, ubiquitous PET increases, decreases (drying) are much more widespread than increases (wetting) in the tropics, subtropics, and midlatitudes in most models, confirming and expanding on earlier findings. The PET increases are also somewhat sensitive to the time resolution of the inputs, although not as systematically as for the PET climatologies. The changes in the balance between P and PET are also quantified using an alternative aridity index, the ratio , which has a one-to-one but nonlinear correspondence with . It is argued that the magnitudes of changes are more uniformly relevant than the magnitudes of changes, which tend to be much higher in wetter regions. The ratio and its changes are also found to be excellent statistical predictors of the land surface evaporative fraction and its changes.

scholarly journals Climate Change Projections of Aridity Conditions in the Iberian Peninsula

2021 ◽  
Author(s):  
Cristina Andrade ◽  
Joana Contente ◽  
João Andrade Santos
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Iberian Peninsula ◽  
Radiative Forcing ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Agroforestry Systems ◽  
Mitigation And Adaptation ◽  
La Mancha

The assessment of aridity conditions is a key factor for water management and the implementation of mitigation and adaptation policies in agroforestry systems. Towards this aim three aridity indices were computed for the Iberian Peninsula (IP): the De Martonne Index (DMI), the Pinna Combinative Index (PCI), and the Erinç Aridity Index (EAI). These three indices were first computed for the baseline period 1961‒1990, using a gridded observational data (E-OBS), and, subsequently, for the periods 2011‒2040 (short-range) and 2041‒2070 (medium-range) using an ensemble of six Regional Climate Models (RCMs) experiments generated by the EURO-CORDEX project. Two Representative Concentration Pathways (RCPs) were analyzed, an intermediate anthropogenic radiative forcing scenario (RCP4.5) and a fossil-intensive emission scenario (RCP8.5). Overall, the three indices disclose a strengthening of aridity and dry conditions in central and southern Iberia until 2070, mainly under RCP8.5. Strong(weak) statistically significant correlations were found between these indices and the total mean precipitation (mean temperature) along with projected significant decreasing(increasing) trends for precipitation(temperature). The prevalence of years with arid conditions (above 70% for 2041‒2070 under both RCPs) are projected to have major impacts in some regions, such as southern Portugal, Extremadura, Castilla-La Mancha, Comunidad de Madrid, Andalucía, Región de Murcia, Comunidad Valenciana, and certain regions within the Aragón province. The projected increase in both the intensity and persistence of aridity conditions in a broader southern half of Iberia will exacerbate the exposure and vulnerability of this region to climate change, while the risk of multi-level desertification should be thoroughly integrated into regional and national water management and planning.

scholarly journals Historically-based run-time bias corrections substantially improve model projections of 100 years of future climate change

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Gerhard Krinner ◽  
Viatcheslav Kharin ◽  
Romain Roehrig ◽  
John Scinocca ◽  
Francis Codron
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Coupled Model ◽  
Atmospheric Model ◽  
Climate Impact ◽  
Future Climate ◽  
Future Climate Change ◽  
Historical Period ◽  
Underlying Assumption ◽  
Bias Corrections

Abstract Climate models and/or their output are usually bias-corrected for climate impact studies. The underlying assumption of these corrections is that climate biases are essentially stationary between historical and future climate states. Under very strong climate change, the validity of this assumption is uncertain, so the practical benefit of bias corrections remains an open question. Here, this issue is addressed in the context of bias correcting the climate models themselves. Employing the ARPEGE, LMDZ and CanAM4 atmospheric models, we undertook experiments in which one centre’s atmospheric model takes another centre’s coupled model as observations during the historical period, to define the bias correction, and as the reference under future projections of strong climate change, to evaluate its impact. This allows testing of the stationarity assumption directly from the historical through future periods for three different models. These experiments provide evidence for the validity of the new bias-corrected model approach. In particular, temperature, wind and pressure biases are reduced by 40–60% and, with few exceptions, more than 50% of the improvement obtained over the historical period is on average preserved after 100 years of strong climate change. Below 3 °C global average surface temperature increase, these corrections globally retain 80% of their benefit.

scholarly journals Eddy Memory Mode of Multidecadal Variability in Residual-Mean Ocean Circulations with Application to the Beaufort Gyre

2017 ◽  
Vol 47 (4) ◽  
pp. 855-866 ◽  
Author(s):  
Georgy E. Manucharyan ◽  
Andrew F. Thompson ◽  
Michael A. Spall
Keyword(s):  
Turbulent Flows ◽  
Climate Models ◽  
Mesoscale Eddies ◽  
Eddy Diffusivity ◽  
Eddy Diffusion ◽  
Diffusion Time ◽  
Observational Constraints ◽  
Ekman Pumping ◽  
Beaufort Gyre ◽  
Finite Memory

AbstractMesoscale eddies shape the Beaufort Gyre response to Ekman pumping, but their transient dynamics are poorly understood. Climate models commonly use the Gent–McWilliams (GM) parameterization, taking the eddy streamfunction to be proportional to an isopycnal slope s and an eddy diffusivity K. This local-in-time parameterization leads to exponential equilibration of currents. Here, an idealized, eddy-resolving Beaufort Gyre model is used to demonstrate that carries a finite memory of past ocean states, violating a key GM assumption. As a consequence, an equilibrating gyre follows a spiral sink trajectory implying the existence of a damped mode of variability—the eddy memory (EM) mode. The EM mode manifests during the spinup as a 15% overshoot in isopycnal slope (2000 km3 freshwater content overshoot) and cannot be explained by the GM parameterization. An improved parameterization is developed, such that is proportional to an effective isopycnal slope , carrying a finite memory γ of past slopes. Introducing eddy memory explains the model results and brings to light an oscillation with a period ≈ 50 yr, where the eddy diffusion time scale TE ~ 10 yr and γ ≈ 6 yr are diagnosed from the eddy-resolving model. The EM mode increases the Ekman-driven gyre variance by γ/TE ≈ 50% ± 15%, a fraction that stays relatively constant despite both time scales decreasing with increased mean forcing. This study suggests that the EM mode is a general property of rotating turbulent flows and highlights the need for better observational constraints on transient eddy field characteristics.

scholarly journals Historical and future trends in wetting and drying in 291 catchments across China

2016 ◽  
Author(s):  
Zhongwang Chen ◽  
Huimin Lei ◽  
Hanbo Yang ◽  
Dawen Yang ◽  
Yongqiang Cao
Keyword(s):  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Meteorological Data ◽  
Coupled Model ◽  
Simulated Data ◽  
Aridity Index ◽  
Global Climate Models ◽  
Simple Method ◽  
Future Evolution

Abstract. The "dry gets drier, wet gets wetter" (DDWW) pattern is a popular catchphrase to summarize hydrologic changes under global warming. However, recent studies based on simulated data have failed to obtain a feasible DDWW pattern for runoff trends. This study tested the DDWW pattern using observed streamflow and meteorological data from 291 catchments in China from 1956 to 2000, interpreted it using a simple method derived from the Budyko hypothesis, and explored its future evolution according to the projections of five global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Similar to the DDWW pattern, the results show that catchments with an aridity index of φ  1 become drier, with nearly 80 % of the studied catchments following this pattern. However, the pattern does not hold in glacier regions due to the effects of melting ice and snow. Based on precipitation and potential evapotranspiration changes, the first-order differential of the Budyko hypothesis can provide a good estimate of runoff changes (R2 = 0.70). Therefore, the atmospheric forcing of water and energy is the key factor in interpreting the DDWW pattern. Over 80 % of the estimated trends have signs coincident with those of the measured trends, implying that the DDWW pattern can be assessed with estimated data. Precipitation is the controlling factor that leads to the DDWW pattern in nearly 90 % of catchments where observed and estimated signs are consistent. In the three tested scenarios (RCP2.6, RCP4.5 and RCP8.5), the different models produce significantly different predicted changes, even under the same scenario, whereas a given model yields similar results under different scenarios. Based on the projected results, the DDWW pattern no longer provides a reliable prediction. However, this conclusion remains tentative due to the large uncertainty of the simulations. The considerable differences between the observed and modelled meteorological data for the same period suggest that this conclusion should be adopted with caution.

scholarly journals SPLINE MODELS OF CONTEMPORARY, 2030, 2060 AND 2090 CLIMATES FOR MICHOACÁN STATE, MÉXICO. IMPACTS ON THE VEGETATION

2012 ◽  
Vol 35 (4) ◽  
pp. 333
Author(s):  
Cuauhtémoc Sáenz-Romero ◽  
Gerald E. Rehfeldt ◽  
Nicholas L. Crookston ◽  
Pierre Duval ◽  
Jean Beaulieu
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Aridity Index ◽  
General Circulation Models ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Emission Scenarios ◽  
Weather Stations

Climate data from 149 weather stations of Michoacán State, at Western México, were extracted from a spline climate model developed for México’s contemporary climate (1961-1990), and for climate projected for the decades centered in years 2030, 2060 and 2090. The model was constructed using outputs from three general circulation models (GCMs: Canadian, Hadley and Geophysical Fluid Dynamics) from two emission scenarios (A “pessimistic” and B “optimistic”). Mean annual temperature (MAT), mean annual precipitation (MAP), annual degree days > 5 °C (DD5), and annual aridity index (DD50.5/MAP) were mapped for Michoacán at an 1 km2 scale, and means were estimated averaging all weather stations. The state average in GCMs and emission scenarios point out that mean annual temperature would increase 1.4 °C by year 2030, 2.2 °C by year 2060 and 3.6 °C by year 2090; whereas annual precipitation would decrease 5.6 % by year 2030, 5.9 % by year 2060 and 7.8 % by year 2090. Climate models can be used for inferring plant-climate relationships and for developing programs to counteract global warming effects. Climate variables were estimated also at Pinus hartwegii and Pinus pseudostrobus growth locations, at Pico de Tancítaro in Central Western Michoacán and Nuevo San Juan Parangaricutiro (near Tancítaro), respectively. According to the annual aridity index values estimated for such locations, it is necessary to conduct assisted migration to match current genotypes to projected climates. This translates into an altitudinal shift of 400 to 450 m higher to match 2030 climates predicted by Canadian Model scenario A2, and 600 to 800 m to match 2060 climates.

scholarly journals Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes

2020 ◽  
Vol 6 (22) ◽  
pp. eaaz6433 ◽  
Author(s):  
Johannes Mülmenstädt ◽  
Christine Nam ◽  
Marc Salzmann ◽  
Jan Kretzschmar ◽  
Tristan S. L’Ecuyer ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Observational Constraints ◽  
Observational Constraint ◽  
Liquid Water Path ◽  
Anthropogenic Aerosols ◽  
Aerosol Forcing ◽  
Warm Rain ◽  
Atmospheric State

Global climate models (GCMs) disagree with other lines of evidence on the rapid adjustments of cloud cover and liquid water path to anthropogenic aerosols. Attempts to use observations to constrain the parameterizations of cloud processes in GCMs have failed to reduce the disagreement. We propose using observations sensitive to the relevant cloud processes rather than only to the atmospheric state and focusing on process realism in the absence of aerosol perturbations in addition to the process susceptibility to aerosols. We show that process-sensitive observations of precipitation can reduce the uncertainty on GCM estimates of rapid cloud adjustments to aerosols. The feasibility of an observational constraint depends on understanding the precipitation intensity spectrum in both observations and models and also on improving methods to compare the two.

scholarly journals Observational constraints on atmospheric and oceanic cross-equatorial heat transports: revisiting the precipitation asymmetry problem in climate models

2015 ◽  
Vol 46 (9-10) ◽  
pp. 3239-3257 ◽  
Author(s):  
Norman G. Loeb ◽  
Hailan Wang ◽  
Anning Cheng ◽  
Seiji Kato ◽  
John T. Fasullo ◽  
...  
Keyword(s):  
Climate Models ◽  
Observational Constraints
Sign in / Sign up

Export Citation Format

Share Document