Surrogate Climate Research Articles

FE-Simulation and Experimental Characterisation of Environmental Effects on the Diffusion and Mechanical Performance of Hyperelastic Adhesive Joints

In operational applications, hyperelastic adhesive joints are exposed to environmental conditions (moisture and temperature) that affect their mechanical performance. The understanding of how the environment can influence the joint durability through both static and cyclic loading is a key aspect to ensure safety and avoid over-dimensioning. The current work presents an investigation of the effect of environment conditions on the diffusion and mechanical performance of two different hyperelastic adhesive joints (a polyurethane and a silicon-modified polymer). To assess the process of moisture mass diffusion, pure adhesive samples were weighted for 387 days when subjected to outdoor weathering conditions. An FEA-diffusion procedure method was demonstrated by (i) predicting the saturation concentration at steady conditions of 40 °C/15% r.h. (40/15) and 40 °C/60% r.h. (40/60), and (ii) predicting the experienced mass change due to outdoor weathering. The reversibility of the effect of conditioning at 40 °C/60% r.h. on the mechanical properties of the adhesives was assessed via quasi-static and fatigue tensile shear testing. The results support the conclusion that conditioning with the surrogate climate of 40 °C/60% r.h. does not cause irreversible damage, as any potential decrease in shear modulus, tensile shear strength and fatigue life due to 40/60 conditioning can be reversed by re-drying at 40/15.

Reservoir Computing as a Tool for Climate Predictability Studies

AbstractReduced‐order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC‐based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine‐learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz‐63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

Effects of Memory Biases on Variability of Temperature Reconstructions

Abstract Quantifying past climate variation and attributing its causes improves our understanding of the natural variability of the climate system. Tree-ring-based proxies have provided skillful and highly resolved reconstructions of temperature and hydroclimate of the last millennium. However, like all proxies, they are subject to uncertainties arising from varying data quality, coverage, and reconstruction methodology. Previous studies have suggested that biological-based memory processes could cause spectral biases in climate reconstructions. This study determines the effects of such biases on reconstructed temperature variability and the resultant implications for detection and attribution studies. We find that introducing persistent memory, reflecting the spectral properties of tree-ring data, can change the variability of pseudoproxy reconstructions compared to the surrogate climate and resolve certain model–proxy discrepancies. This is especially the case for proxies based on ring-width data. Such memory inflates the difference between the Medieval Climate Anomaly and the Little Ice Age and suppresses and extends the cooling in response to volcanic eruptions. When accounting for memory effects, climate model data can reproduce long-term cooling after volcanic eruptions, as seen in proxy reconstructions. Results of detection and attribution studies show that signals in reconstructions as well as residual unforced variability are consistent with those in climate models when the model fingerprints are adjusted to reflect autoregressive memory as found in tree rings.

Potential Vorticity Diagnostics to Quantify Effects of Latent Heating in Extratropical Cyclones. Part II: Application to Idealized Climate Change Simulations

Abstract It is still debated how enhanced cloud-condensational latent heating (LH) in a warmer and moister climate may affect the dynamics of extratropical cyclones. In this study, a diagnostic method that explicitly quantifies the contribution of LH to the lower-tropospheric cyclonic potential vorticity (PV) anomaly is used to investigate the effects of stronger LH on the dynamics, intensity, and impacts of cyclones in two conceptually different sets of idealized climate change simulations. A first set of regional surrogate climate change simulations of individual moderate to intense Northern Hemisphere cyclones in a spatially homogeneously 4-K-warmer climate reveals that enhanced LH can largely but not exclusively explain the substantially varying increase in intensity and impacts of most of these cyclones. A second set of idealized aquaplanet GCM simulations demonstrates that the role of enhanced LH becomes multifaceted for large ensembles of cyclones if climate warming is additionally accompanied by changes in the horizontal and vertical temperature structure: cyclone intensity increases with warming due to the continuous increase in LH, reaches a maximum in climates warmer than present day, and decreases beyond a certain warming once the increase of LH is overcompensated by the counteracting reduction in mean available potential energy. Because of their substantially stronger increase in LH, the most intense cyclones reach their maximum intensity in warmer climates than moderately intense cyclones with weaker LH. This suggests that future projections of the extreme tail of the storm tracks might be particularly sensitive to a correct representation of LH.

Ramifications of Atmospheric Humidity on Monsoon Depressions over the Indian Subcontinent

In this study, a comprehensive investigation is carried out to examine the sensitivity of tropospheric relative humidity (RH) on monsoon depressions (MDs) under a changing climate regime through surrogate climate change approach over the Indian region. Composite analysis of four MDs show a persistent warming (RH2+) and cooling (RH2−) throughout the troposphere in the sensitivity experiments. In-depth analysis of a MD over the Arabian Sea (AS) exhibits sustained warming for RH2+, which is accredited to 2.6% increase in stratiform clouds accounting for 13% increment in heating, whereas 5% increment in convective clouds hardly contribute to total heating. Frozen hydrometeors (graupel and snow) are speculated to be the major contributors to this heating. Stratiform clouds showed greater sensitivity to RH perturbations in the lower troposphere (1000–750 hPa), albeit very less sensitivity for convective clouds, both in the lower and mid-troposphere (700–500 hPa). Precipitation is enhanced in a moist situation (RH2+) owing to positive feedbacks induced by moisture influx and precipitation efficiency, while negative feedbacks suppressed precipitation in a dry troposphere (RH2−). In a nutshell, it is inferred that under moist (dry) situations, it is highly likely that intense (weak) MDs will occur in the near future over the Indian region.

The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models

Abstract. Climate models project an increase in heavy precipitation events in response to greenhouse gas forcing. Important elements of such events are rain showers and thunderstorms, which are poorly represented in models with parameterized convection. In this study, simulations with 12 km horizontal grid spacing (convection-parameterizing model, CPM) and 2 km grid spacing (convection-resolving model, CRM) are employed to investigate the change in the diurnal cycle of convection with warmer climate. For this purpose, simulations of 11 days in June 2007 with a pronounced diurnal cycle of convection are compared with surrogate simulations from the same period. The surrogate climate simulations mimic a future climate with increased temperatures but unchanged relative humidity and similar synoptic-scale circulation. Two temperature scenarios are compared: one with homogeneous warming (HW) using a vertically uniform warming and the other with vertically dependent warming (VW) that enables changes in lapse rate.The two sets of simulations with parameterized and explicit convection exhibit substantial differences, some of which are well known from the literature. These include differences in the timing and amplitude of the diurnal cycle of convection, and the frequency of precipitation with low intensities. The response to climate change is much less studied. We can show that stratification changes have a strong influence on the changes in convection. Precipitation is strongly increasing for HW but decreasing for the VW simulations. For cloud type frequencies, virtually no changes are found for HW, but a substantial reduction in high clouds is found for VW. Further, we can show that the climate change signal strongly depends upon the horizontal resolution. In particular, significant differences between CPM and CRM are found in terms of the radiative feedbacks, with CRM exhibiting a stronger negative feedback in the top-of-the-atmosphere energy budget.

A virtual climate library of surface temperature over North America for 1979\u20132015

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life.

Relative impacts of land use and climate change on summer precipitation in the Netherlands

Abstract. The effects of historic and future land use on precipitation in the Netherlands are investigated on 18 summer days with similar meteorological conditions. The days are selected with a circulation type classification and a clustering procedure to obtain a homogenous set of days that is expected to favor land impacts. Changes in precipitation are investigated in relation to the present-day climate and land use, and from the perspective of future climate and land use. To that end, the weather research and forecasting (WRF) model is used with land use maps for 1900, 2000, and 2040. In addition, a temperature perturbation of +1 °C assuming constant relative humidity is imposed as a surrogate climate change scenario. Decreases in precipitation of, respectively, 3–5 and 2–5 % are simulated following conversion of historic to present, and present to future, land use. The temperature perturbation under present land use conditions increases precipitation amounts by on average 7–8 % and amplifies precipitation intensity. However, when also considering future land use, the increase is reduced to 2–6 % on average, and no intensification of extreme precipitation is simulated. In all, the simulated effects of land use changes on precipitation in summer are smaller than the effects of climate change, but are not negligible.

Precipitation response of monsoon low‐pressure systems to an idealized uniform temperature increase

AbstractThe monsoon low‐pressure systems (LPSs) are one of the most rain‐bearing synoptic‐scale systems developing during the Indian monsoon. We have performed high‐resolution, convection‐permitting experiments of 10 LPS cases with the Weather Research and Forecasting regional model, to investigate the effect of an idealized uniform temperature increase on the LPS intensification and precipitation. Perturbed runs follow a surrogate climate change approach, in which a uniform temperature perturbation is specified, but the large‐scale flow and relative humidity are unchanged. The differences between control and perturbed simulations are therefore mainly due to the imposed warming and moisture changes and their feedbacks to the synoptic‐scale flow. Results show that the LPS precipitation increases by 13%/K, twice the imposed moisture increase, which is on the same order as the Clausius‐Clapeyron relation. This large precipitation increase is attributed to the feedbacks in vertical velocity and atmospheric stability, which together account for the high sensitivity. In the perturbed simulations the LPSs have higher propagation speeds and are more intense. The storms intensification to the uniform temperature perturbation can be interpreted in terms of the conditional instability of second kind mechanism where the condensational heating increases along with low‐level convergence and vertical velocity in response to temperature and moisture increases. As a result, the surface low deepens.

Low-pressure systems and extreme precipitation in central India: sensitivity to temperature changes

Extreme rainfall events in the central Indian region are often related to the passage of synoptic scale monsoon low-pressure systems (LPS). This study uses the surrogate climate change method on ten monsoon LPS cases connected to observed extreme rainfall events, to investigate how sensitive the precipitation and runoff are to an idealized warmer and moister atmosphere. The ten cases are simulated with three different initial and lateral boundary conditions: the unperturbed control run, and two sets of perturbed runs where the atmospheric temperature is increased uniformly throughout the atmosphere, the specific humidity increased according to Clausius Clapeyron’s relation, but the large-scale flow is unchanged. The difference between the control and perturbed simulations are mainly due to the imposed warming and feedback influencing the synoptic flow. The mean precipitation change with warming in the central Indian region is 18–20 %/K, with largest changes at the end of the LPS tracks. The LPS in the warmer runs are bringing more moisture further inland that is released as precipitation. In the perturbed runs the precipitation rate is increasing at all percentiles, and there is more frequent rainfall with very heavy intensities. This leads to a shift in which category that contributes most to the total precipitation: more of the precipitation is coming from the category with very heavy intensities. The runoff changes are similar to the precipitation changes, except the response in intensity of very heavy runoff, which is around twice the change in intensity of very heavy precipitation.

Surrogate Climate Change Scenario and Projections with a Regional Climate Model: Impact on the Aridity in South America

The impact of global warming on the aridity in South America (SA) is investigated. For this purpose, the methodology for generating surrogate climate-change scenarios with a RCM is employed. For the present climate (CTRL) the RCM is initialized with and driven by ECMWF/ERA-Interim reanalysis data. Two aridity indices are used: the Budyko and the UNEP indices. The results for the CTR are in agreement with other model studies which indicate future warming; rainfall increases in southeastern South America, Ecuador and Peru and decreases in the central and eastern Amazon. In general the model reproduces the aridity in the continent compared with the observed data for both indices. The distribution of aridity over SA in surrogate climate-change scenario shows an increase of the dryness in the continent. Over Amazonia the aridity increases 23.9% (for the UNEP index) and 3.1% (for the Budyko index), suggesting that portions of the Amazonia forest are replaced by dry land area. The semi-arid zone over northeast Brazil expands westward, attaining the interior of north Brazil. In this region the aridity increases 20% (for the UNEP index) and 0.6% (for the Budyko index) indicating that areas of humid regime may be occupied by areas with dry land regime. The RCM was also integrated driven by the AOGCM ECHAM5/MPI-OM for the reference climate (CTRL2) and under A1B SRES scenario. The results for the present-day climate are similar in CTRL2 and CTRL, and are in agreement with CRU data. The distribution of the aridity for the present climate seems to be better represented in CTRL using both Budyko and UNEP indices. The changes in aridity (future climate minus control) are higher in the run forced by the A1B SRES scenario. Although the UNEP and Budyko indices show potentialities and limitations to represent the aridity distribution over SA, the changes in aridity due to a pseudo-scenario of global warming are higher using the UNEP index.

The CGILS experimental design to investigate low cloud feedbacks in general circulation models by using single‐column and large‐eddy simulation models

A surrogate climate change is designed to investigate low cloud feedbacks in the northeastern Pacific by using Single Column Models (SCMs), Cloud Resolving Models (CRMs), and Large Eddy Simulation models (LES), as part of the CGILS study (CFMIP‐GASS Intercomparison of LES and SCM models). The constructed large‐scale forcing fields, including subsidence and advective tendencies, and their perturbations in the warmer climate are shown to compare well with conditions in General Circulation Models (GCMs), but they are free from the impact of any GCM parameterizations. The forcing fields in the control climate are also shown to resemble the mean conditions in the ECMWF‐Interim Reanalysis. Applications of the forcing fields in SCMs are presented. It is shown that the idealized design can offer considerable insight into the mechanisms of cloud feedbacks in the models. Caveats and advantages of the design are also discussed.

Paleoclimate and bubonic plague: a forewarning of future risk?

Pandemics of bubonic plague have occurred in Eurasia since the sixth century ad. Climatic variations in Central Asia affect the population size and activity of the plague bacterium's reservoir rodent species, influencing the probability of human infection. Using innovative time-series analysis of surrogate climate records spanning 1,500 years, a study in BMC Biology concludes that climatic fluctuations may have influenced these pandemics. This has potential implications for health risks from future climate change.See research article http://www.biomedcentral.com/1741-7007/8/112

Local effects of climate change over the Alpine region: A study with a high resolution regional climate model with a surrogate climate change scenario

We discuss a surrogate climate change (SCC) simulation over the Alpine region with a high resolution regional climate model (RegCM3) using a fine scale sub‐grid land surface representation. Multi‐year simulations are completed with an imposed illustrative 3K warming on the forcing large scale meteorological conditions and on the sea surface temperatures in the interior domain. The corresponding relative humidity is kept constant, which results in an increase of atmospheric moisture. We find that in the winter season precipitation increases consistently with the SCC approach, with a significant dependence on topographical elevation. Other components of the surface energy and water budgets also show a marked elevation dependency, mostly tied to changes in snow cover. In summer, contrary to what might be expected from the SCC forcing, precipitation decreases over the Alpine mountain chain. This is due to a local surface‐atmosphere feedback mechanism involving reduced snow cover and soil moisture at the beginning of summer. Our results suggest that over the Alps during summer local feedbacks related to the surface energy and water budgets are important factors in determining the precipitation response to global warming. This result might extend to other mid‐latitude mountainous areas.

Computational methods for parameter estimation in climate models

Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. The performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. We show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate prediction, particularly those where sampling is limited by availability of computational resources.

Comparison of climate field reconstruction techniques: application to Europe

This paper presents a comparison of principal component (PC) regression and regularized expectation maximization (RegEM) to reconstruct European summer and winter surface air temperature over the past millennium. Reconstruction is performed within a surrogate climate using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) 1.4 and the climate model ECHO-G 4, assuming different white and red noise scenarios to define the distortion of pseudoproxy series. We show how sensitivity tests lead to valuable “a priori” information that provides a basis for improving real world proxy reconstructions. Our results emphasize the need to carefully test and evaluate reconstruction techniques with respect to the temporal resolution and the spatial scale they are applied to. Furthermore, we demonstrate that uncertainties inherent to the predictand and predictor data have to be more rigorously taken into account. The comparison of the two statistical techniques, in the specific experimental setting presented here, indicates that more skilful results are achieved with RegEM as low frequency variability is better preserved. We further detect seasonal differences in reconstruction skill for the continental scale, as e.g. the target temperature average is more adequately reconstructed for summer than for winter. For the specific predictor network given in this paper, both techniques underestimate the target temperature variations to an increasing extent as more noise is added to the signal, albeit RegEM less than with PC regression. We conclude that climate field reconstruction techniques can be improved and need to be further optimized in future applications.

Comments on “Testing the Fidelity of Methods Used in Proxy-Based Reconstructions of Past Climate”

Abstract Mann et el. found that a version of the Regularized Expectation Maximization (RegEM) method to reconstruct the temperatures of the last millennium showed similar results to previous reconstructions in one of their earlier papers. They also tested the RegEM method in the surrogate climate of a simulation with the Climate System Model (CSM) and found no attenuation of the pseudoreconstructed centennial variability of the Northern Hemisphere mean temperature compared to the one simulated by the model. This is in contrast with the results by von Storch et al., who found, in a simulation with ECHO-G model, that the earlier Mann et al. method underestimates the centennial temperature variability of the Northern Hemisphere temperature. The newer paper by Mann et al. explains that this difference is in part due to the unrealistic character of the ECHO-G simulation. However, it is shown here that similar results to those of von Storch et al. are also found in an ECHO-G simulation that closely resembles the CSM simulation used by Mann et al. Therefore, it is argued here that this discrepancy could be related to other factors, probably to the use of a longer calibration period and to the difference between RegEM and the original method by Mann et al.

Testing a European winter surface temperature reconstruction in a surrogate climate

We evaluate the skill of a European winter surface air temperature reconstruction over the last 500 years using pseudoproxies obtained from the ECHO‐G and HadCM3 climate models. The emphasis is thereby on the effect of the reduction of available predictors back in time, an issue that has not yet been investigated in detail at continental and seasonal scale. It is found that the key factor in determining the reconstruction skill is the number of predictors and particularly their spatial distribution. However considering the usually insufficient spatial and temporal predictor availability in paleo‐reconstructions, the quality of the predictors becomes more important further back in time. Not surprisingly, the lowest reconstruction skill is found in the early period when the predictor network is reduced. Important differences between ECHO‐G and HadCM3‐based pseudoproxy reconstructions are discussed and implications for future analyses are presented.

Testing statistical downscaling methods in simulated climates

The consistency of two statistical downscaling methods and two different predictors to estimate past (last millennium) and future (21st century) precipitation in the Iberian and Scandinavian Peninsulas is assessed in the surrogate climate of a coupled climate model simulation. The methods are based on canonical correlation analysis and the search for analogs, with sea level pressure (SLP) and 500 mb geopotential height as predictors. The precipitation downscaled by the statistical methods within the simulated climate is compared with the direct model output. The estimation based on SLP alone agrees with the modeled precipitation on the Iberian Peninsula. However, the estimates for Scandinavia in the 21st century are drier than the target precipitation. Geopotential height as predictor performs worse than SLP in both regions yielding too dry future climate. Differences in both regions are analyzed in terms of relative humidity, which contributes to future precipitation changes of Scandinavian precipitation.

Reconstructing past climate from noisy data.

Empirical reconstructions of the Northern Hemisphere (NH) temperature in the past millennium based on multiproxy records depict small-amplitude variations followed by a clear warming trend in the past two centuries. We use a coupled atmosphere-ocean model simulation of the past 1000 years as a surrogate climate to test the skill of these methods, particularly at multidecadal and centennial time scales. Idealized proxy records are represented by simulated grid-point temperature, degraded with statistical noise. The centennial variability of the NH temperature is underestimated by the regression-based methods applied here, suggesting that past variations may have been at least a factor of 2 larger than indicated by empirical reconstructions.