Climatological Model Research Articles

Residential exposure to solar ultraviolet radiation and risk of childhood hematological malignancies in Switzerland: A census-based cohort study.

Still little is known about possible environmental risk factors of childhood hematological malignancies (CHM). Previous studies suggest that ultraviolet radiation (UVR) exposure is associated with a lower risk of acute lymphoblastic leukemia (ALL) in children. We investigated the association between solar UVR exposure and risk of CHM in Switzerland, a country with greatly varying topography and weather conditions. We included all resident children aged 0-15 years from the Swiss National Cohort during 1990-2016 and identified incident cancer cases through probabilistic record linkage with the Swiss Childhood Cancer Registry. We estimated the overall annual mean UV level and the mean level for the month of July during 2004-2018 at children's homes using a climatological model of the midday (11 am-3 pm) UV-index (UVI) with a spatial resolution of 1.5-2 km. Using risk-set sampling, we obtained a nested case-control data set matched by birth year and fitted conditional logistic regression models (virtually equivalent to analyzing full cohort data using proportional hazards models) adjusting for sex, neighborhood socio-economic position, urbanization, air pollution, and background ionizing radiation. Our analyses included 1446 cases of CHM. Estimated adjusted hazard ratios (HR) per unit increase in UVI in July were 0.76 (95% CI 0.59-0.98) for leukemia and 0.74 (0.55-0.98) for ALL. Results for annual exposure were similar but confidence intervals were wider and included one. We found no evidence for an association for lymphoma overall (HR 1.14, 95% CI 0.59-2.19 for annual exposure) or diagnostic subgroups. Our study provides further support for an inverse association between exposure to ambient solar UVR and childhood ALL.

Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth

Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.

A Markov model approach for statistical tropical cyclone wind radii baseline forecasts

Abstract A skill baseline for five-day, 34-, 50-, and 64-knot (1 kt = 0.514 m s−1) tropical cyclone (TC) wind radii forecasts is described. The Markov Model CLiper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial forecast track and intensity as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the North Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–2022 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic, and eastern North Pacific basins, and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use.

Arctic September Sea Ice Concentration Biases in CMIP6 Models and Their Relationships with Other Model Variables

Abstract The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models.

How Do Substorms Influence Hemispheric Asymmetries in Equivalent Currents?

AbstractIonospheric dynamics exhibits a distinct hemispheric asymmetry, influenced primarily by the Interplanetary Magnetic Field (IMF) By component, dipole tilt, or a combination of both. Previous studies have indicated a reduction in these asymmetries during substorms. In this study, we conduct a superposed epoch analysis using ground magnetometer data from the northern hemisphere to examine the impact of substorms on ionospheric current asymmetry. This analysis uses the assumption of mirror symmetry between the northern and southern hemispheres when IMF By and dipole tilt are reversed. We observe a significant reduction in nightside equivalent current asymmetry indicating the IMF By and dipole tilt have minimal influence on the substorm current. On the other hand, we find that substorms exert minimal or negligible effects on dayside currents. This difference in response between nightside and dayside currents emphasizes the need to incorporate nightside dynamics into existing climatological models, which presently rely mainly on upstream parameters due to a lack of robust parameters effectively representing them. Our findings provide important insights for future modeling efforts, highlighting the distinct interactions between substorms and ionospheric currents across different hemispheric regions.

A statistical study of precipitation on the eastern Antarctic plateau (Dome-C) using remote sensing and in-situ instrumentation.

Studying precipitation at very high latitudes is a challenge, particularly during the polar winter. Direct monitoring of ice habit and size in high latitude precipitation is crucial for validating the algorithms used to derive precipitation from radar, and for improving the climatological modeling of polar areas. The high plateau lacks long-term direct observations of precipitation. In this work, carried out at Concordia Station (Dome-C (DC), -75°S, 123°E, 3233 m a.m.s.l), the use of a depolarization LIDAR, a flatbed scanner (ICECAMERA), a microwave profiler (HAMSTRAD) and meteorological instrumentation made possible the study, over the period 2014-2021, of shape, size, height and temperature of formation of precipitation. The precipitation sources were classified into four types: ice fogs, liquid fogs, mixed-phase clouds, and cirrus. Ten representative ice habits for Dome-C were chosen. The size distribution for every habit was calculated, allowing for the estimation of the corresponding radar reflectivity. The use of W-band radars, such as CLOUDSAT, with a sensitivity of -28dB, resulted in capturing all the crystals observed in Concordia. A positive trend was observed between grain size and height in ice habits that are typical of cloud precipitation. North West (NW) and North East (NE) winds at cloud height, blowing from coastal regions, caused the majority of precipitation from clouds. The study also examined the height trend of the ice habit composition of precipitation. The ice habit composition for each of the four types of precipitation source was analyzed, and the possibility of determining the source by simply observing the precipitation was explored. This work marks the first comprehensive investigation of precipitation on the eastern Antarctic plateau.

A Parallelized Climatological Drifter-Based Model of Sargassum Biomass Dynamics in the Tropical Atlantic

A Parallelized Climatological Drifter-Based Model of Sargassum Biomass Dynamics in the Tropical Atlantic

The Influence of Space Traffic on AIM/CIPS PMC Frequencies at 80°N

AbstractWe explore the effects of lower thermospheric water vapor deposited by launch vehicle plumes on polar mesospheric cloud (PMC) frequencies at 80°N. We use July‐averaged PMC frequencies from 2007 to 2022 from the Cloud Imaging and Particle Size (CIPS) instrument on NASA's Aeronomy of Ice in the Mesosphere (AIM) satellite. Launch sites worldwide are typically located near northern mid‐latitudes. Using the orbital launch record for the same time period, we find that the number of launches correlates with PMC frequencies with a coefficient of r = 0.60, which increases to r = 0.75 when only selecting launches from 2.5 to 21.5 local time (LT), indicating a weak LT dependence on global‐scale transport to 80°N. To support our findings, we use meridional winds from the Michelson Interferometer for Global High‐resolution Imaging experiment on NASA's Ionospheric Connection Explorer satellite and winds from the Horizontal Wind Model climatology to interpret the northward motion of air parcels at 105 km. We find the launch LT window that maximizes the correlation coefficient to be consistent with the expected maximum northward motion from the diurnal variation of mid‐latitude meridional winds. Comparisons with Microwave Limb Sounder satellite observations of upper mesospheric temperature and water vapor reveal a strong dependence of cloud frequency on water vapor (r = 0.86) but not on temperature (r = −0.26), indicating that water vapor is the primary source of PMC variability for the bright PMCs at 80°N. We therefore find that launch vehicle plumes originating primarily from northern mid‐latitudes modulate PMC frequency at 80°N in July.

Global WaterPack - The development of global surface water over the past 20 years at daily temporal resolution

Open surface water across the globe is essential for many life forms and is an important source for human settlements, agriculture, and industry. The presence and variation in time and space is influenced by different natural conditions (e.g. climate, topography, geology) and human use (e.g. irrigation, flood protection). The information on the spatial and temporal distribution of open surface water is fundamental for many disciplines and is also required as an essential parameter for hydrological and climatological modelling. Here, we present a dataset derived from satellite earth observation, which is based on more than 6.3 million single MODIS products with a volume of approx. 300 TB. The resulting dataset reflects the situation of open surface water on a global scale for each day over the time period from 2003 to 2022 at a spatial resolution of 250 m. The dataset enables the analysis of the development of lake and reservoir surface areas, freezing cycles, and inundation areas.

A major midlatitude hurricane in the Little Ice Age

Abstract. An unusually severe hurricane (Louisbourg Storm) struck Nova Scotia, Canada, in 1757. Historic records describing storm conditions as well as damage to ships and coastal fortifications indicate an intensity beyond any modern (post-1851) Atlantic cyclones striking the same region, yet this storm struck during a cold climate period known as the Little Ice Age (LIA). Its track and timing coincided with a British naval blockade of a French fleet at Fortress Louisbourg during the Seven Years' War (1756–1763). This provides a unique opportunity to explore growing scientific evidence of heightened storminess in the North Atlantic despite a colder climate expected to suppress hurricane intensification but which research is increasingly showing to have supported North Atlantic storms of exceptional strength. Weather attributes extracted from the logs of naval vessels scattered by the Louisbourg Storm provided multiple hourly observations recorded at different locations. Wave height and wind force estimates at ship locations were compared to extreme storm surge heights calculated for Louisbourg Harbour and a shipwreck site south of Fortress Louisbourg. Comparing these metrics to those of modern analogues that crossed the same bathymetry reflects landfall intensity consistent with a powerful major hurricane. Historical records show this storm originated as a tropical cyclone at the height of hurricane season and intensified into the northern midlatitudes along the Gulf Stream. Its intensity at landfall is consistent with established seasonal climatological models where highly baroclinic westerlies driven by autumn continental cooling encounter intensifying north-tracking tropical cyclones fuelled by sea surface temperatures that peak in autumn. Stronger seasonal contrasts from earlier and colder continental westerlies in the Little Ice Age (LIA) may have triggered explosive extratropical transition from a large hurricane resulting in a more severe strike. It suggests that tropical cyclones lasting days to weeks and the conditions that generate them are likely masked by cooler historic mean annual to multi-decadal LIA climate reconstructions.

Ionospheric Nowcasting Over Italy Through Data Assimilation: A Synergy Between IRI UP and IONORING

AbstractAn accurate modeling of the ionosphere electron density is pivotal to guarantee the effective operation of communication and navigation systems, particularly during Space Weather events. Despite the crucial contribution of empirical models like the International Reference Ionosphere (IRI), their limitations in predicting ionospheric variability, especially under geomagnetically disturbed conditions, are acknowledged. The solution proposed in this work involves integrating real‐time, spatially distributed ionospheric measurements into climatological models through data assimilation. To enhance our predictive capabilities, we present an upgrade of the IRI UP data‐assimilation method, incorporating real‐time vertical total electron content (vTEC) maps from the IONORING algorithm for nowcasting ionospheric conditions over Italy. This approach involves updating the IRI F2‐layer peak electron density description through ionospheric indices, to finally produce real‐time maps over Italy of the ordinary critical frequency of the F2‐layer, foF2, which is crucial for radio‐propagation applications. The IRI UP–IONORING method performance has been evaluated against different climatological and nowcasting models, and under different Space Weather conditions, by showing promising outcomes which encourages its inclusion in the portfolio of ionospheric real‐time products available over Italy. The validation analysis highlighted also what are the current limitations of the IRI UP–IONORING method, particularly during nighttime for severely disturbed conditions, suggesting avenues for future enhancements.

Exploring ionospheric dynamics: a comprehensive analysis of GNSS TEC estimations during the solar phases using linear function model

Abstract The modeling and forecasting of Total Electron Content (TEC) play a major role in influencing signals from satellite-based navigation systems and impact the performance of diverse satellite-dependent technologies. The intensity of solar ionizing radiation and the state of geomagnetic field activity influence the Global Navigation Satellite System (GNSS)-TEC. This paper uses a Linear TEC Function (LTF) climatology model to understand ionospheric behavior under solar and geomagnetic activities that cause variations in the electron distribution of the ionosphere medium. The LTF model integrates representations of solar EUV photon (MgII) and geomagnetic (SYMH) activities, incorporating solar-modulated oscillations (periodic variations) at four seasonal cycles and a linear trend. The LTF model examined the time series of GPS-TEC at a location (geographic 34.95° N, 134.05° E) with a time resolution of 1 h, from 1997 to 2016, covering solar cycles 23 and 24. The Root Mean Square Deviation (RMSD) and correlation coefficient between the GNSS-TEC and model TEC (LTF) was 5.30 TECU and 95 %. The results indicate that solar components, as well as annual and semi-annual variations, have a significant impact on the daily average TEC. Solar activity appears to be the predominant determining factor of TEC during the solar phases of cycles 23 and 24. In contrast, periodic influences primarily outline TEC during periods characterized by minimal solar activity. The geomagnetic component presents an increased influence, particularly during storm periods. The model demonstrates superior performance in Total TEC modeling compared to other state-of-the-art approaches.

Analysis of the Interaction of Oceanic Cloudiness with the Upper Oceanic Stratum

Objective: This paper addressed the relationship between two physical quantities of interest in climatology: the thermal energy present in the upper ocean stratum (UOS) and the oceanic cloudiness (OC), i.e., the one located above oceans. Methods: The interplay between these physical quantities was pointed out by analyzing the time series of seasonal and diurnal anomalies of ocean's total cloud cover, and the anomalies of the thermal energy present in the UOS. We examined these time series to identify signals indicative of interactions between the UOS and OC. We then aimed to explain these interactions at a climatological level. Finally, for validation, we demonstrated that our explanations are consistent with a global climatological model that we developed. Results: It was demonstrated that in both cases, the time series could be described as a Markov-1 alternating type, exhibiting similar structures. This led to the conclusion that the OC served as a natural thermostat in relation to the thermal energy contained in the UOS. By using a simple mathematical model previously introduced in a recent paper by the author (2023) to account for the thermal exchanges between the troposphere and the UOS, we confirmed that the OC acted as a natural thermostat for the thermal energy in the UOS. This "natural thermostat" effect, incorporated in the model, resulted in simulations projecting significantly less warming by 2095 compared to those from most existing global climate models (GCMs). Conclusion: Methodologically, this paper confirmed the interest of using Markov chains to identify climatological interactions. The developed GCM, utilized for validating hypotheses explaining these interactions, proved simple and efficient for simulating key climatological parameters in the evolution of the global climate. On a strictly scientific level, the work's main contribution lies in providing a definitive answer about cloud cover feedback in global warming, establishing it as significantly negative.

The role of soil moisture-temperature coupling for the 2018 Northern European heatwave in a subseasonal forecast

This study investigates the predictability of the 2018 Northern Europe heatwave using the GloSea5 forecast model from the perspective of land-atmosphere interactions. We focus on an inverse relationship wherein soil drying leads to increased temperatures and the model's ability to simulate this hypersensitivity in the soil moisture-temperature coupling on the dry side of a breakpoint defined as the soil moisture threshold below which land feedbacks nonlinearly amplify extreme heat. When evaluating forecast model performance in predicting this heatwave, we compare deterministic forecast scores (Hit Rate (HR) and True Skill Score (TSS)) for whether model Surface Soil Moisture (SSM) falls within the hypersensitive regime. GloSea5 exhibits enhanced prediction skill for the extreme heat event when the modelled soil moisture is within the hypersensitive regime. To understand the skill of the heatwave forecast for hit and missed cases of capturing SSM below the breakpoint, we first evaluate the climatological model performance for the water- and energy-limited processes, and then perform a comparison classified by whether SSM verifies on the dry side of the wilting point. The composite analysis demonstrates that the reproducibility of the breakpoint is tied to an improvement in climatological land coupling processes, mainly for classification in the water-limited coupling regime. Therefore, the results suggest that the process-based connection between soil moisture and temperature is a potential source for improving heatwave forecasts on subseasonal to seasonal (S2S) time scales.

Climatological Observation and Model Simulation of Near-Surface Hourly Maximum Gust Wind in Northern China

Abstract While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts.

Recent Challenges in the APCC Multi‐Model Ensemble Seasonal Prediction: Hindcast Period Issue

AbstractSeasonal forecasts are commonly issued in the form of anomalies, which are departures from the average over a specified multiyear reference period (climatology). The model climatology is estimated as the average of the retrospective forecasts over the hindcast period. However, different operational centers that provide seasonal ensemble predictions use different hindcast periods based on their model climatology. Additionally, the hindcast periods of recently developed and upgraded newer models have shifted in the recent years. In this paper, we discuss the recent challenges faced by APCC multi‐model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various experiments for MME prediction, we propose changing the hindcast period, which is the most appropriate solution for APCC operation. This makes the newly developed models join the MME and increases the total number of participating models, which facilitates the skill improvement of the MME prediction.

Deterministic and stochastic tendency adjustments derived from data assimilation and nudging

AbstractWe develop and compare model‐error representation schemes derived from data assimilation increments and nudging tendencies in multidecadal simulations of the Community Atmosphere Model, version 6. Each scheme applies a bias correction during simulation runtime to the zonal and meridional winds. We quantify the extent to which such online adjustment schemes improve the model climatology and variability on daily to seasonal timescales. Generally, we observe about a 30% improvement to annual upper‐level zonal winds, with largest improvements in boreal spring (around 35%) and winter (around 47%). Despite only adjusting the wind fields, we additionally observe around 20% improvement to annual precipitation over land, with the largest improvements in boreal fall (around 36%) and winter (around 25%), and around 50% improvement to annual sea‐level pressure, globally. With mean‐state adjustments alone, the dominant pattern of boreal low‐frequency variability over the Atlantic (the North Atlantic Oscillation) is significantly improved. Additional stochasticity increases the modal explained variances further, which brings the variability closer to the observed value. A streamfunction tendency decomposition reveals that the improvement is due to an adjustment to the high‐ and low‐frequency eddy–eddy interaction terms. In the Pacific, the mean‐state adjustment alone led to an erroneous deepening of the Aleutian low, but this was remedied with the addition of stochastically selected tendencies. Finally, from a practical standpoint, we discuss the performance of using data assimilation increments versus nudging tendencies for an online model‐error representation.

The Spatio-Temporal Dynamics of Water Resources (Rainfall and Snow) in the Sierra Nevada Mountain Range (Southern Spain)

This paper describes the use of a unique spatio-temporally resolved precipitation and temperature dataset to assess the spatio-temporal dynamics of water resources over a period of almost seven decades across the Sierra Nevada mountain range, which is the most southern Alpine environment in Europe. The altitude and geographical location of this isolated alpine environment makes it a good detector of climate change. The data were generated by applying geostatistical co-kriging to significant instrumental precipitation and temperature (minimum, maximum and mean) datasets. The correlation between precipitation and altitude was not particularly high and the statistical analysis yielded some surprising results in the form of mean annual precipitation maps and yearly precipitation time series. These results confirm the importance of orographic precipitation in the Sierra Nevada mountain range and show a decrease in mean annual precipitation of 33 mm per decade. Seasonality, however, has remained constant throughout the period of the study. The results show that previous studies have overestimated the altitudinal precipitation gradient in the Sierra Nevada and reveal its complex spatial variability. In addition, the results show a clear correspondence between the mean annual precipitation and the NAO index and, to a much lesser extent, the WeMO index. With respect to temperature, there is a high correlation between minimum temperature and altitude (coefficient of correlation = −0.84) and between maximum temperature and altitude (coefficient of correlation = −0.9). Thus, our spatial temperature maps were very similar to topographic maps, but the temporal trend was complex, with negative (decreasing) and positive (increasing) trends. A dynamic model of snowfall can be obtained by using the degree-day methodology. These results should be considered when checking the local performance of climatological models.

Estimation of Surface Sensible Heat Flux due to Precipitation over CONUS and Its Impact on Urban Extreme Precipitation Modeling

Abstract The surface sensible heat flux induced by precipitation (QP) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.

Multi-decadal pacemaker simulations with an intermediate-complexity climate model

Abstract. In this paper, we first describe the main features of a new version of the International Centre for Theoretical Physics global atmospheric model (SPEEDY) with improved simulation of surface fluxes and the formulation of a three-layer thermodynamic ocean model (TOM3) suitable to explore the coupled extratropical response to tropical ocean variability. Then, we present results on the atmospheric model climatology, highlighting the impact of the modifications introduced in the model code, and show how important features of interdecadal and interannual variability are simulated in a “pacemaker” coupled ensemble of 70-year runs, where portions of the tropical Indo-Pacific are constrained to follow the observed variability. Despite the very basic representation of variations in greenhouse forcing and heat transport to the deep ocean (below the 300 m domain of the TOM3 model), the coupled ensemble reproduces the variations in surface temperature over land and sea with very good accuracy, confirming the role of the Indo-Pacific as a “pacemaker” for the natural fluctuations of global-mean surface temperatures found in earlier studies. Atmospheric zonal-mean temperature trends over 50 years are also realistically simulated in the extratropical lower troposphere and up to 100 hPa in the tropics. On the interannual scale, sea-surface temperature (SST) variability in sub-tropical and tropical regions not affected by SST relaxation is underestimated (mostly because of the absence of dynamically induced variability), while extratropical SST variability during the cold seasons is comparable to that observed. Atmospheric teleconnection patterns and their connections with SST are reproduced with high fidelity, although with local differences in the amplitude of regional features (such as a larger-than-observed response of extratropical SST to North Atlantic Oscillation variability). The SPEEDY-TOM3 model also reproduces the observed connection between averages of surface heat fluxes over the oceans and land surface air temperature in the wintertime northern extratropics. Overall, as in earlier versions of SPEEDY, the fidelity of the simulations (both in terms of climatological means and variability) is higher near the surface and in the lower troposphere, while the negative impacts of the coarse vertical resolution and simplified parameterizations are mostly felt in the stratosphere. However, the improved simulation of surface heat fluxes and their impact on extratropical SST variability in this model version (obtained at a very modest computational cost) make the SPEEDY-TOM3 model a suitable tool to investigate the coupled response of the extratropical circulation to interannual and inter-decadal changes of tropical SST in ensemble experiments.