Climate Model Hindcast Research Articles

Challenges in the Detection and Attribution of Northern Hemisphere Surface Temperature Trends Since 1850

Since 2007, the Intergovernmental Panel on Climate Change (IPCC) has heavily relied on the comparison between global climate model hindcasts and global surface temperature (ST) estimates for concluding that post-1950s global warming is mostly human-caused. In Connolly et al., we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance (TSI) and ST data sets. We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST, one could (prematurely) conclude anything from the warming being “mostly human-caused” to “mostly natural.” Richardson and Benestad suggested our analysis was “erroneous” and “flawed” because we did not use a multilinear regression. They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC’s attribution statement. They also objected that many of the published TSI data sets were out-of-date. However, here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series, the original conclusions of Connolly et al. are confirmed for all five ST data sets. Therefore, it is still unclear whether the observed warming is mostly human-caused, mostly natural or some combination of both.

Analysis of the Atmosphere and the Ocean Upper Layer State Predictability for up to 5 Years Ahead Using the INMCM5 Climate Model Hindcasts

Analysis of the Atmosphere and the Ocean Upper Layer State Predictability for up to 5 Years Ahead Using the INMCM5 Climate Model Hindcasts

A study of climate model responses of the western Pacific subtropical high to El Niño diversity

This study examines a climate model hindcast of the responses of the western Pacific subtropical high (WPSH) to three types of El Nino events: the Eastern Pacific (EP) El Nino and the types I and II of Central Pacific (CP-I and CP-II) El Nino. These El Nino types differ from each other in the central locations and patterns of their sea surface temperature (SST) anomalies. They invoke four different mechanisms to impact the WPSH. Hindcasts produced by the Taiwan Central Weather Bureau (CWB) Climate Forecast System 1-tiered model (TCWB1T1) are analyzed. These hindcasts realistically simulate the impacts on the WPSH during the CP-I El Nino, but overestimate the impacts during the decaying summer of the EP El Nino and during the developing autumn and early winter of the CP-II El Nino. The overestimates are mainly the result of an overly strong Maritime Continent regional circulation mechanism during EP El Ninos and an overly strong Indian Ocean capacitor mechanism during CP-II El Ninos. Further analyses show that these model biases are related to biases in the simulations of El Nino SST anomalies and the Walker circulation. Both model deficiencies are common to contemporary coupled climate models.

A Continuous Record of Central Tropical Pacific Climate Since the Midnineteenth Century Reconstructed From Fanning and Palmyra Island Corals: A Case Study in Coral Data Reanalysis

AbstractAccurate estimation of central tropical Pacific (CTP) climate variability on interannual to centennial time scales is required for robust projections of future global climate trends. Here we outline an approach that blends instrumental and coral proxy observations to yield a continuous, monthly resolved record of climate evolution in the CTP spanning the past 160 years. We concatenate coral oxygen isotope (δ18O) records from multiple living and fossil corals collected from Fanning Island (4°N, 160°W) and Palmyra Island (5°N; 162°W) located in the heart of the El Niño–Southern Oscillation. We use the regularized expectation maximization (RegEM) method to impute missing data across short gaps of 5 to 23 years within and beyond individual coral records. The resulting monthly resolved Fanning/Palmyra Island climate record spans continuously from 1863 to 2016 and provides an example of how extended time series can be built from shorter coral segments. The extended record highlights the strong trend toward warmer and wetter mean conditions in late twentieth century, in agreement with the majority of climate model hindcast simulations. The continuous reconstruction also enables a direct comparison of four exceptionally strong El Niño events (1877–1878, 1940–1941, 1997–1998, and 2015–2016). Three of these very strong El Niño events in the CTP featured a precursor warm event in the prior year and that may have favored the development of a strong El Niño event.

Mediterranean winter rainfall in phase with African monsoons during thepast 1.36million years.

Mediterranean climates are characterized by strong seasonal contrasts between dry summers and wet winters. Changes in winter rainfall are critical for regional socioeconomic development, but are difficult to simulate accurately1 and reconstruct on Quaternary timescales. This is partly because regional hydroclimate records that cover multiple glacial-interglacial cycles2,3 with different orbital geometries, global ice volume and atmospheric greenhouse gas concentrations are scarce. Moreover, the underlying mechanisms of change and their persistence remain unexplored. Here we show that, over the past 1.36million years, wet winters in the northcentral Mediterranean tend to occur with high contrasts in local, seasonal insolation and a vigorous African summer monsoon. Our proxy time series from Lake Ohrid on the Balkan Peninsula, together with a 784,000-year transient climate model hindcast, suggest that increased sea surface temperatures amplify local cyclone development and refuel North Atlantic low-pressure systems that enter the Mediterranean during phases of low continental ice volume and high concentrations of atmospheric greenhouse gases. A comparison with modern reanalysis data shows that current drivers of the amount of rainfall in the Mediterranean share some similarities to those that drive the reconstructed increases in precipitation. Our data cover multiple insolation maxima and are therefore an important benchmark for testing climate model performance.

Evaluating the Bias of South China Sea Summer Monsoon Precipitation Associated with Fast Physical Processes Using a Climate Model Hindcast Approach

AbstractThe present study aims to identify the precipitation bias associated with the interactions among fast physical processes in the Community Atmospheric Model, version 5 (CAM5), during the abrupt onset of the South China Sea (SCS) summer monsoon, a key precursor of the overall East Asia summer monsoon (EASM). The multiyear hindcast approach is utilized to obtain the well-constrained synoptic-scale horizontal circulation each year during the onset period from the years 1998 to 2012. In the pre-onset period, the ocean precipitation over the SCS is insufficiently suppressed in CAM5 hindcasts and thus weaker land–ocean precipitation contrasts. This is associated with the weaker and shallower convection simulated over the surrounding land, producing weaker local circulation within the SCS basin. In the post-onset period, rainfall of the organized convection over the Philippine coastal ocean is underestimated in the hindcasts, with overestimated upper-level heating. These biases are further elaborated as the underrepresentation of the convection diurnal cycle and coastal convection systems, as well as the issue of precipitation sensitivity to environmental moisture during the SCS onset period. The biases identified in hindcasts are consistent with the general bias of the EASM in the climate simulation of CAM5. The current results highlight that the appropriate representation of land–ocean–convection interactions over coastal areas can potentially improve the simulation of seasonal transition over the monsoon regions.

Long-Term Atmospheric Changes in a Convection-Permitting Regional Climate Model Hindcast Simulation over Northern Germany and the German Bight

Long-term atmospheric changes are a result of complex interactions on various spatial scales. In this study, we examine the long-term variability of the most important meteorological variables in a convection-permitting regional climate model simulation. A consistent, gridded data set from 1948 to 2014 was computed using the regional climate model COSMO-CLM with a very high convection-permitting resolution at a grid distance of 2.8 km, for a region encompassing the German Bight and Northern Germany. This is one of the very first atmospheric model simulations with such high resolution, and covering several decades. Using a very high-resolution hindcast, this study aims to extend knowledge of the significance of regional details for long-term variability and multi-decadal trends of several meteorological variables such as wind, temperature, cloud cover, precipitation, and convective available potential energy (CAPE). This study demonstrates that most variables show merely large decadal variability and no long-term trends. The analysis shows that the most distinct and significant positive trends occur in temperature and in CAPE for annual mean values as well as for extreme events. No clear and no significant trend is detectable for the annual sum of precipitation and for extreme precipitation. However, spatial structures in the trends remain weak.

Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?

ABSTRACTQuantile mapping (QM) is routinely applied in many climate change impact studies for the bias correction (BC) of daily precipitation data. It corrects the complete distribution, but does not correct for errors in the annual cycle. Therefore, QM is often applied separately to temporal subsamples of the data (e.g. each calendar month), which reduces the calibration sample size. The question arises whether this sample size reduction negates the benefit from applying QM to temporal subsamples. We applied four QM methods in a cross‐validation approach to 40 years of daily precipitation data from 10 regional climate model (RCM) hindcast runs, without and with (semi‐annual, seasonal, and monthly) subsampling. QM subsampling improved the BC of daily RCM precipitation; less distinct for independent data but considerably for the calibration data. The optimal subsampling timescale for the correction of independent data depended on the chosen QM method and ranged between semi‐annual and monthly. Overall, a sub‐annual QM improves the forcing for climate change impact studies and thus their reliability.

Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions

Commonly termed “added value”, the additional regional details gained by high-resolution regional climate models (RCMs) over the coarser resolution reanalysis driving data are often indistinguishable at the 0.44° grid mesh computationally affordable large CORDEX domains. In an attempt to highlight the benefits of finer resolutions to study the RCM added value, five North American weather phenomena are evaluated in RCM hindcast simulations using grid meshes of 0.44°, 0.22° and 0.11° with available observations. The results show that the orographic precipitation on the west coast of North America is enhanced and more realistic, with two distinct rain bands in the finer resolution simulation. The spatial distribution of precipitation in August and the high frequency of summer precipitation extremes over southwestern United States reveal that the North American monsoon is improved with increasing resolution. Only the finer RCM simulation shows skill at producing snowbelts around the Great Lakes by capturing lake-effect snow. A comparison of wind roses in the St. Lawrence River Valley indicates that only the finer RCM simulation is able to reproduce wind channeling by resolving complex orography. Finally, the simulation of the summer land-sea breezes by the RCM simulations leads to added value in the diurnal cycle of precipitation over the Florida peninsula and the Caribbean islands. Overall, the almost systematic improvements of the finer resolution simulations suggest that higher resolutions, only computationally affordable over smaller domains, might get a higher priority to promote RCM added value.

Hail potential in Europe based on a regional climate model hindcast

AbstractDue to the local‐scale nature of hail and a lack of appropriate observation systems, comprehensive, reliable, and consistent information about hail frequency and intensity in Europe is not available. To overcome this constraint, we developed a logistic hail model that quantifies the potential of the atmosphere to form hailstorms. The model is based on a combination of appropriate hail‐relevant meteorological parameters. This paper presents the application of an adjusted version of the logistic model with the objective being to estimate the hail potential across Europe based on dynamically downscaled National Centers for Environmental Prediction/National Center for Atmospheric Research1 reanalysis over a long‐term period of 60 years (1951–2010). The model output, in terms of the potential hail index (PHI), identified several hot spots that are well known from other observational studies. Time series of the PHI over the 60 year period show a high correlation at different sites across Europe and high annual and multiannual variability, but no overall trend.

An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models

AbstractWe present an improved procedure of generating initial conditions (ICs) for climate model hindcast experiments with specified sea surface temperature and sea ice. The motivation is to minimize errors in the ICs and lead to a better evaluation of atmospheric parameterizations' performance in the hindcast mode. We apply state variables (horizontal velocities, temperature, and specific humidity) from the operational analysis/reanalysis for the atmospheric initial states. Without a data assimilation system, we apply a two‐step process to obtain other necessary variables to initialize both the atmospheric (e.g., aerosols and clouds) and land models (e.g., soil moisture). First, we nudge only the model horizontal velocities toward operational analysis/reanalysis values, given a 6 h relaxation time scale, to obtain all necessary variables. Compared to the original strategy in which horizontal velocities, temperature, and specific humidity are nudged, the revised approach produces a better representation of initial aerosols and cloud fields which are more consistent and closer to observations and model's preferred climatology. Second, we obtain land ICs from an off‐line land model simulation forced with observed precipitation, winds, and surface fluxes. This approach produces more realistic soil moisture in the land ICs. With this refined procedure, the simulated precipitation, clouds, radiation, and surface air temperature over land are improved in the Day 2 mean hindcasts. Following this procedure, we propose a “Core” integration suite which provides an easily repeatable test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modeled cloud‐associated processes relative to observations.

Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

Abstract Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illinois. In summer, the RCMs are unable to capture the distribution width and long warm tails for the coastal location of Los Angeles, California, while long cold tails are poorly realized for Houston, Texas. The evaluation results are repeated using two additional reanalysis products adjusted by station observations and two standard reanalysis products to assess the impact of observational uncertainty. Results are robust when compared with those obtained using the adjusted reanalysis products as reference, while larger uncertainties are introduced when standard reanalysis is employed as reference. Model biases identified in this work will allow for further investigation into associated mechanisms and implications for future simulations of temperature extremes.

Validation of climate model-inferred regional temperature change for late-glacial Europe.

Comparisons of climate model hindcasts with independent proxy data are essential for assessing model performance in non-analogue situations. However, standardized paleoclimate datasets for assessing the spatial pattern of past climatic change across continents are lacking for some of the most dynamic episodes of Earth's recent past. Here we present a new chironomid-based paleotemperature dataset designed to assess climate model hindcasts of regional summer temperature change in Europe during the late-glacial and early Holocene. Latitudinal and longitudinal patterns of inferred temperature change are in excellent agreement with simulations by the ECHAM-4 model, implying that atmospheric general circulation models like ECHAM-4 can successfully predict regionally diverging temperature trends in Europe, even when conditions differ significantly from present. However, ECHAM-4 infers larger amplitudes of change and higher temperatures during warm phases than our paleotemperature estimates, suggesting that this and similar models may overestimate past and potentially also future summer temperature changes in Europe.

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

Abstract Surface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation.

A regional climate model hindcast for Siberia: analysis of snow water equivalent

Abstract. This study analyzes the added value of a regional climate model hindcast with respect to snow water equivalent (SWE) for Siberia when compared to SWE estimates from forcing NCEP-R. In addition, we examine the discrepancies of simulated SWE to several recent reanalysis products (NCEP-R2 NCEP-CFSR, ERA-Interim). We apply the regional climate model COSMO-CLM (CCLM) to a 50 km grid spacing using NCEP-R1 as driving force to obtain a 63 yr (1948 to 2010) gridded dataset of historical SWE. Simulated regional climate data is necessary because of the absence of station data in that region. To perform large-scale assessments we use the satellite-derived daily SWE product of ESA DUE GlobSnow from 1987 to 2010. Russian station SWE data is used for cross-checking the findings. In January (mid-winter), the SWE hindcast is in good agreement with GlobSnow, whereas it overestimates SWE during the melting season. CCLM shows a clear added value in providing realistic SWE information compared to the driving reanalysis. The temporal consistency of CCLM is higher than that presented by ERA-Interim and NCEP-R2.

Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Monthly-mean precipitation, mean (TAVG), maximum (TMAX) and minimum (TMIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for TAVG and TMAX than for TMIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.

Constrains on sea surface temperature reconstructions of the Last Glacial Maximum ocean and their comparison with climate model hindcasts

Constrains on sea surface temperature reconstructions of the Last Glacial Maximum ocean and their comparison with climate model hindcasts

The influence of spectral nudging on typhoon formation in regional climate models

Regional climate models can successfully simulate tropical cyclones and typhoons. This has been shown and was evaluated for hindcast studies of the past few decades. But often global and regional weather phenomena are not simulated at the observed location, or occur too often or seldom even though the regional model is driven by global reanalysis data which constitute a near-realistic state of the global atmosphere. Therefore, several techniques have been developed in order to make the regional model follow the global state more closely. One is spectral nudging, which is applied for horizontal wind components with increasing strength for higher model levels in this study. The aim of this study is to show the influence that this method has on the formation of tropical cyclones (TC) in regional climate models. Two ensemble simulations (each with five simulations) were computed for Southeast Asia and the Northwestern Pacific for the typhoon season 2004, one with spectral nudging and one without. First of all, spectral nudging reduced the overall TC number by about a factor of 2. But the number of tracks which are similar to observed best track data (BTD) was greatly increased. Also, spatial track density patterns were found to be more similar when using spectral nudging. The tracks merge after a short time for the spectral nudging simulations and then follow the BTD closely; for the no nudge cases the similarity is greatly reduced. A comparison of seasonal precipitation, geopotential height, and temperature fields at several height levels with observations and reanalysis data showed overall a smaller ensemble spread, higher pattern correlations and reduced root mean square errors and biases for the spectral nudged simulations. Vertical temperature profiles for selected TCs indicate that spectral nudging is not inhibiting TC development at higher levels. Both the Madden–Julian Oscillation and monsoonal precipitation are reproduced realistically by the regional model, with results slightly closer to reanalysis data for the spectral nudged simulations. On the basis of this regional climate model hindcast study of a single typhoon season, spectral nudging seems to be favourable since it has mostly positive effects on typhoon formation, location and general circulation patterns in the generation areas of TCs.

Hydrologic drainage of the Greenland Ice Sheet

AbstractA simple hydrologic drainage network for the Greenland Ice Sheet is modelled from available digital elevation models (DEMs) of bedrock, and surface topography and assumptions of hydrostatic water pressure, uniform hydraulic conductivity, and no conduit flow within the ice sheet. As such, it is a first‐order model best suited for broad‐scale hydrological assessment. Results identify 293 distinct hydrologic basins (185–117 000 km2) together with their ‘realized’ (wet) and ‘unrealized’ (dry) drainage patterns. Intersection with 1991–2000 Polar MM5 (PMM5) mesoscale climate model hindcasts of meltwater runoff suggest that these basins route varying amounts of water to the ice edge, ranging from 0 to 16 km3 annually and totalling 242 km3/year for the entire ice sheet. Regionally speaking, average annual volumetric meltwater production (km3/year) is highest in southwest and lowest in northeast Greenland, with greater hydrologic activity in western regions than in eastern regions for a given latitude. The extent to which meltwater truly reaches the ice margin as modelled is difficult to test. However, the simulated flow outlet locations show qualitative agreement with the locations of 460 observed meltwater outlets (proglacial lakes, streams, and rivers; and sediment plumes into fjörds) mapped continuously along the ice sheet perimeter. On average, about 36% of the modelled drainage network was activated (i.e. received water) over the 1991–2000 study period. Remaining areas, barring dynamic changes to ice‐surface topography, would presumably activate if surface melt penetrates deeper into the ice sheet interior. Both new datasets are freely available for scientific use at the National Snow and Ice Data Center (ftp://sidads.colorado.edu/pub/DATASETS/parca/nsidc‐0372‐hydrologic‐outlets; ftp://sidads.colorado.edu/pub/DATASETS/parca/nsidc‐0371‐hydrologic‐sub‐basins). Copyright © 2009 John Wiley & Sons, Ltd.