A Performance Evaluation of Potential Intensity over the Tropical Cyclone Passage to South Korea Simulated by CMIP5 and CMIP6 Models

Potential intensity (PI) is a metric for climate model evaluation of TC-related thermodynamic conditions. However, PI is utilized usually for assessing basin-wide TC-related thermodynamic conditions, and not for evaluating TC passage to a certain region. Here we evaluate model-simulated PI over the passage of TCs affecting South Korea (KOR PI) as well as the PI over the entire western North Pacific basin (WNP PI) using 25 CMIP5 and 27 CMIP6 models. In terms of pattern correlations and bias-removed root mean square errors, CMIP6 model performances for KOR PI are found to be noticeably improved over CMIP5 models in contrast to negligible improvement for WNP PI, although it is not in terms of normalized standard deviations. This implies that thermodynamic condition on the route of TCs affecting South Korea is likely better captured by CMIP6 models than CMIP5 models.

Evaluating the impact of rainfall–runoff model structural uncertainty on the hydrological rating of regional climate model simulations

We propose to evaluate the impact of rainfall–runoff model (RRM) structural uncertainty on climate model evaluation, performed within a process-oriented framework using the RRM. Structural uncertainty is assessed with an ensemble approach using three conceptual RRMs (HBV, IHACRES and GR4J). We evaluate daily precipitation and temperature from 11 regional climate models forced by five general circulation models (GCM–RCMs), issued from EURO-CORDEX. The assessment was performed over the reference period (1970–2000) for five catchments situated in northern Tunisia. Seventeen discharge performance indexes were used to explore the representation of hydrological processes. The three RRMs performed well over the reference period, with Nash–Sutcliffe efficiency values ranging from 0.70 to 0.90 and bias close to 0%. The ranking of GCM–RCMs according to hydrological performance indexes is more meaningful before the bias correction, which considerably reduces the differences between GCM- and RCM-driven hydrological simulations. Our results illustrate a strong similarity between the different RRMs in terms of raw GCM–RCM performances over the reference period for the majority of performance indexes, in spite of their different model structures. This proves that the structural uncertainty induced by RRMs does not affect GCM–RCM evaluation and ranking, which contributes to consolidate the RRM as a standard tool for climate model evaluation.

A new framework for identifying and investigating seasonal climate extremes

Previous studies have recognized the societal relevance of climatic extremes on the seasonal timescale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet or warm seasons). However, these findings have not yet been generalized beyond case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogues to even the most extreme ERA-Interim events.This approach offers numerous opportunities for systematically analyzing large samples of extreme seasons, and several such analyses are illustrated. We reveal a striking co-occurrence of El Niño to La Niña transitions and the largest ERA-Interim mid-latitude extreme summer events. Moreover, we perform a climate model evaluation with regard to extreme season size and intensity measures and estimate how often an extreme winter like the cold North American 2013/14 winter is expected anywhere in mid-latitude regions. Furthermore, we present a large set of simulated analogues to this event, which allow studying commonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified.

A new framework for identifying and investigating seasonal climate extremes

<p>Previous studies have recognized the societal relevance of climatic extremes on the seasonal timescale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet or warm seasons). However, these findings have not yet been generalizing beyond individual case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogues to even the most extreme ERA-Interim events, which allow for climatological analyses of their statistical and physical characteristics.</p><p>This approach offers numerous opportunities for analyzing large samples of extreme seasons across regions, and several such analyses are illustrated exemplarily. We perform a climate model evaluation with regard to extreme season size and intensity measures and estimate how often an extreme winter like the cold North American 2013/14 winter is expected anywhere in mid-latitude regions. Moreover, we present a large set of simulated spatial analogues to this event, which allows to study commonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified.</p>

The ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

Seasonal transition dates can reveal biases in Arctic sea ice simulations

Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, opening, freeze onset, freeze-up, and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

The observational uncertainty in sea ice concentration estimates from remotely sensed passive microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarization from standard output of an Earth System Model. To evaluate sources of uncertainties when applying ARC3O, we compare brightness temperatures simulated by applying ARC3O on three assimilation runs of the MPI Earth System Model (MPI-ESM), assimilated with three different sea ice concentration products, with brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that the simulated and observed brightness temperatures differ up to 10 K in the period between October and June, depending on the region and the assimilation run. We show that these discrepancies between simulated and observed brightness temperature can be attributed mainly to the underlying observational uncertainty in sea ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea ice concentration and the melt pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable of realistically translating the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.