scholarly journals The distinct problems of physical inconsistency and of multivariate bias potentially involved in the statistical adjustment of climate simulations

2021 ◽  
Author(s):  
Mégane Alavoine ◽  
Patrick Grenier
Keyword(s):  
Climate Model ◽  
Statistical Properties ◽  
Specific Humidity ◽  
Multivariate Techniques ◽  
Physical Constraint ◽  
Bias Adjustment ◽  
Multivariate Technique ◽  
Statistical Adjustment ◽  
Climate Simulations ◽  
Adjustment Strategies

Bias adjustment of numerical climate model simulations involves several technical and epistemological arguments wherein the notion of physical inconsistency is often referred to, either for rejecting the legitimacy of bias adjustment in general or for justifying the necessity of sophisticated multivariate techniques. However, this notion is often mishandled, in part because the literature generally proceeds without defining it. In this context, the central objective of this study is to clarify and illustrate the distinction between physical inconsistency and multivariate bias, by investigating the effect of bias adjustment on two different kinds of inter-variable relationships, namely a physical constraint expected to hold at every step of a time series and statistical properties that emerge with potential bias over a climatic time scale. The study involves the application of 18 alternative bias adjustment techniques on 10 climate simulations and over 12 sites across North America. Adjusted variables are temperature, pressure, relative humidity and specific humidity, linked by a thermodynamic constraint. The analysis suggests on the one hand that a clear instance of potential physical inconsistency can be avoided with either a univariate or a multivariate technique, if and only if the bias adjustment strategy explicitly considers the physical constraint to be preserved. On the other hand, it also suggests that sophisticated multivariate techniques alone aren’t complete adjustment strategies in presence of a physical constraint, as they cannot replace its explicit consideration. As a supplementary objective, this study relates common optional adjustment procedures with likely effects on diverse basic statistical properties, as an effort to guide climate information users in the determination of adequate bias adjustment strategies for their research purposes.

Midlatitude cyclones in convection permitting climate simulations: the added value offered for extreme wind speeds and sting-jets

2021 ◽  
Author(s):  
Colin Manning ◽  
Elizabeth Kendon ◽  
Hayley Fowler ◽  
Nigel Roberts ◽  
Segolene Berthou ◽  
...  
Keyword(s):  
Climate Model ◽  
Surface Wind ◽  
Added Value ◽  
Large Contribution ◽  
High Wind ◽  
Wind Speeds ◽  
Wind Gusts ◽  
Climate Simulations ◽  
Extreme Wind ◽  
Extreme Wind Speeds

<p>Extra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a phenomenon known as a sting-jet account for some of the most damaging storms. A sting-jet (SJ) is a mesoscale core of high wind speeds that occurs in particular types of cyclones, specifically Shapiro-Keyser (SK) cyclones, and can produce extremely damaging surface wind gusts. High-resolution climate models are required to adequately model SJs and so it is difficult to gauge their contribution to current and future wind risk. In this study, we develop a low-cost methodology to automate the detection of sting jets, using the characteristic warm seclusion of SK cyclones and the slantwise descent of high wind speeds, within pan-European 2.2km convection-permitting climate model (CPM) simulations. Following this, we quantify the contribution of such storms to wind risk in Northern Europe in current and future climate simulations, and secondly assess the added value offered by the CPM compared to a traditional coarse-resolution climate model. This presentation will give an overview of the developed methods and the results of our analysis.</p><p>Comparing with observations, we find that the representation of wind gusts is improved in the CPM compared to ERA-Interim reanalysis data. Storm severity metrics indicate that SK cyclones account for the majority of the most damaging windstorms. The future simulation produces a large increase (>100%) in the number of storms exceeding high thresholds of the storm metric, with a large contribution to this change (40%) coming from windstorms in which a sting-jet is detected. Finally, we see a systematic underestimation in the GCM compared to the CPM in the frequency of extreme wind speeds at 850hPa in the cold sector of cyclones, likely related to better representation of sting-jets and the cold conveyor belt in the CPM. This underestimation is between 20-40% and increases with increasing wind speed above 35m/s. We conclude that the CPM adds value in the representation of severe surface wind gusts, providing more reliable future projections and improved input for impact models.</p>

scholarly journals Cross-validation of bias-corrected climate simulations is misleading

2018 ◽  
Vol 22 (9) ◽  
pp. 4867-4873 ◽  
Author(s):  
Douglas Maraun ◽  
Martin Widmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Cross Validation ◽  
Climate Model ◽  
False Negative ◽  
Internal Variability ◽  
Validation Period ◽  
Climate Simulations ◽  
Free Running ◽  
The Difference

Abstract. We demonstrate both analytically and with a modelling example that cross-validation of free-running bias-corrected climate change simulations against observations is misleading. The underlying reasoning is as follows: a cross-validation can have in principle two outcomes. A negative (in the sense of not rejecting a null hypothesis), if the residual bias in the validation period after bias correction vanishes; and a positive, if the residual bias in the validation period after bias correction is large. It can be shown analytically that the residual bias depends solely on the difference between the simulated and observed change between calibration and validation periods. This change, however, depends mainly on the realizations of internal variability in the observations and climate model. As a consequence, the outcome of a cross-validation is also dominated by internal variability, and does not allow for any conclusion about the sensibility of a bias correction. In particular, a sensible bias correction may be rejected (false positive) and a non-sensible bias correction may be accepted (false negative). We therefore propose to avoid cross-validation when evaluating bias correction of free-running bias-corrected climate change simulations against observations. Instead, one should evaluate non-calibrated temporal, spatial and process-based aspects.

scholarly journals Future Changes in the Ozone Quasi-Biennial Oscillation with Increasing GHGs and Ozone Recovery in CCMI Simulations

2017 ◽  
Vol 30 (17) ◽  
pp. 6977-6997 ◽  
Author(s):  
Hiroaki Naoe ◽  
Makoto Deushi ◽  
Kohei Yoshida ◽  
Kiyotaka Shibata
Keyword(s):  
Zonal Wind ◽  
Climate Model ◽  
Vertical Shear ◽  
Quasi Biennial Oscillation ◽  
The Past ◽  
Meteorological Research Institute ◽  
Climate Simulations ◽  
The Future ◽  
Ozone Depleting Substances ◽  
Biennial Oscillation

The future quasi-biennial oscillation (QBO) in ozone in the equatorial stratosphere is examined by analyzing transient climate simulations due to increasing greenhouse gases (GHGs) and decreasing ozone-depleting substances under the auspices of the Chemistry–Climate Model Initiative. The future (1960–2100) and historical (1979–2010) simulations are conducted with the Meteorological Research Institute Earth System Model. Three climate periods, 1960–85 (past), 1990–2020 (present), and 2040–70 (future) are selected, corresponding to the periods before, during, and after ozone depletion. The future ozone QBO is characterized by increases in amplitude by 15%–30% at 5–10 hPa and decreases by 20%–30% at 40 hPa, compared with the past and present climates; the future and present ozone QBOs increase in amplitude by up to 60% at 70 hPa, compared with the past climate. The increased amplitude at 5–10 hPa suggests that the temperature-dependent photochemistry plays an important role in the enhanced future ozone QBO. The weakening of vertical shear in the zonal wind QBO is responsible for the decreased amplitude at 40 hPa in the future ozone QBO. An interesting finding is that the weakened zonal wind QBO in the lowermost tropical stratosphere is accompanied by amplified QBOs in ozone, vertical velocity, and temperature. Further study is needed to elucidate the causality of amplification about the ozone and temperature QBOs under climate change in conditions of zonal wind QBO weakening.

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

scholarly journals Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

2018 ◽  
Vol 22 (6) ◽  
pp. 3175-3196 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Grid Cell ◽  
Dependence Structure ◽  
Historical Period ◽  
Temporal Properties ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Site Dependence

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell  ×  number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

scholarly journals Assessment of Regional Global Climate Model Water Vapor Bias and Trends Using Precipitable Water Vapor (PWV) Observations from a Network of Global Positioning Satellite (GPS) Receivers in the U.S. Great Plains and Midwest

2012 ◽  
Vol 25 (16) ◽  
pp. 5471-5493 ◽  
Author(s):  
Jacola A. Roman ◽  
Robert O. Knuteson ◽  
Steven A. Ackerman ◽  
David C. Tobin ◽  
Henry E. Revercomb
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Precipitable Water ◽  
Specific Humidity ◽  
Model Output ◽  
Global Positioning ◽  
The U.S

Abstract Precipitable water vapor (PWV) observations from the National Center of Atmospheric Research (NCAR) SuomiNet networks of ground-based global positioning system (GPS) receivers and the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) are used in the regional assessment of global climate models. Study regions in the U.S. Great Plains and Midwest highlight the differences among global climate model output from the Fourth Assessment Report (AR4) Special Report on Emissions Scenarios (SRES) A2 scenario in their seasonal representation of column water vapor and the vertical distribution of moisture. In particular, the Community Climate System model, version 3 (CCSM3) is shown to exhibit a dry bias of over 30% in the summertime water vapor column, while the Goddard Institute for Space Studies Model E20 (GISS E20) agrees well with PWV observations. A detailed assessment of vertical profiles of temperature, relative humidity, and specific humidity confirm that only GISS E20 was able to represent the summertime specific humidity profile in the atmospheric boundary layer (&lt;3%) and thus the correct total column water vapor. All models show good agreement in the winter season for the region. Regional trends using station-elevation-corrected GPS PWV data from two complimentary networks are found to be consistent with null trends predicted in the AR4 A2 scenario model output for the period 2000–09. The time to detect (TTD) a 0.05 mm yr−1 PWV trend, as predicted in the A2 scenario for the period 2000–2100, is shown to be 25–30 yr with 95% confidence in the Oklahoma–Kansas region.

scholarly journals Evaluating quantile-based bias adjustment methods for climate change scenarios

2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Climate Change Signal ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Statistical Bias ◽  
Bias Adjustment ◽  
Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

scholarly journals A general framework for understanding the response of the water cycle to global warming over land and ocean

2013 ◽  
Vol 10 (12) ◽  
pp. 15263-15294 ◽  
Author(s):  
M. L. Roderick ◽  
F. Sun ◽  
W. H. Lim ◽  
G. D. Farquhar
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Climate Models ◽  
Climate Model ◽  
Water Cycle ◽  
Specific Humidity ◽  
Water Vapour Content ◽  
Long Wave ◽  
Small Change

Abstract. Climate models project increases in globally averaged atmospheric specific humidity at the Clausius–Clapeyron (CC) value of around 7% K−1 whilst projections for precipitation (P) and evaporation (E) are somewhat muted at around 2% K−1. Such global projections are useful summaries but do not provide guidance at local (grid box) scales where impacts occur. To bridge that gap in spatial scale, previous research has shown that the following relation, Δ(P − E) &amp;propto; P − E, holds for zonal averages in climate model projections. In this paper we first test whether that relation holds at grid box scales over ocean and over land. We find that the zonally averaged relation does not hold at grid box scales. We further find that the zonally averaged relation does not hold over land – it is specific to zonal averages over the ocean. As an alternative we tested whether the long-standing Budyko framework of catchment hydrology could be used to synthesise climate model projections over land. We find that climate model projections of Δ(P − E) out to the year 2100 conform closely to the Budyko framework. The analysis also revealed that climate models project little change in the net irradiance at the surface. To understand that result we examined projections of the key surface energy balance terms. In terms of global averages, we find the climate model projections are dominated by changes in only three terms of the surface energy balance; an increase in the incoming longwave irradiance while the responses are (mostly) restricted to the outgoing longwave irradiance with a small change in the evaporative flux. Because the change in outgoing longwave irradiance is a function of the change in surface temperature, we show that the precipitation sensitivity (i.e. 2% K−1) is an accurate summary of the partitioning of the greenhouse-induced surface forcing. With that we demonstrate that the precipitation sensitivity (2% K−1) is less than the CC value (7% K−1) because most of the greenhouse-induced surface forcing is partitioned into outgoing longwave irradiance (instead of evaporation). In essence, the models respond to elevated [CO2] by an increase in atmospheric water vapour content that increases the incoming long-wave irradiance at the surface. The surface response is dominated by a near equal increase in outgoing long-wave irradiance with only minor changes in other terms of the surface energy balance.

Sign in / Sign up

Export Citation Format

Share Document