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Fiaz Hussain, Gokmen Ceribasi, Ahmet Iyad Ceyhunlu, Ray-Shyan Wu, Muhammad Jehanzeb Masud Cheema, Rana Shahzad Noor, Muhammad Naveed Anjum, Muhammad Azam, and Arslan Afzal

Abstract

The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, environment, and water resources. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for precipitation trend detection at eleven stations located in Soan River Basin (SRB), Potohar region Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting and calculating monthly shifts under different trend behaviors i.e. increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows complex nature of behaviour and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first data set and the second data set in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behaviour in the water resources and agricultural sectors.

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Iury T. Simoes-Sousa, Amit Tandon, Jared Buckley, Debasis Sengupta, Sree Lekha J, Emily Shroyer, and Simon P. de Szoeke

Abstract

Atmospheric cold pools, generated by evaporative downdrafts from precipitating clouds, are ubiquitous in the Bay of Bengal. We use data from three moorings near 18°N to characterize a total of 465 cold pools. The cold pools are all dry, with a typical temperature drop of 2°C (max. 5°C) and specific humidity drop of 1 g/kg (max. 6 g/kg). Most cold pools last 1.5-3.5 hours (max. 14 hours). Cold pools occur almost every day in the North Bay from April to November, principally in the late morning, associated with intense precipitation that accounts for 80% of total rain. They increase the latent heat flux to the atmosphere by about 32 W/m2(median), although the instantaneous enhancement of latent heat flux for individual cold pools reaches 150 W/m2 . During the rainiest month (July), the cold pools occur 21% of the time and contribute nearly 14% to the mean evaporation. A composite analysis of all cold pools shows that the temperature and specific humidity anomalies are responsible for ~90% of the enhancement of sensible and latent heat flux, while variations in wind speed are responsible for the remainder. Depending on their gust front speed, the estimated height of the cold pools primarily ranges from 850 to 3200 m, with taller fronts more likely to occur during the summer monsoon season (Jun-Sep). Our results indicate that the realistic representation of cold pools in climate models is likely to be important for improved simulation of air-sea fluxes and monsoon rainfall.

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David N. Whiteman, Kofi Boateng, Sara Harbison, Hadijat Oke, Audrey Rappaport, Monique Watson, Ayomiposi Ajayi, Oluwafisayo Okunuga, Ricardo Forno, and Marcos Andrade

Abstract

For the past four years, four different cohorts of students from the Science and Technology program at Eleanor Roosevelt High School in Greenbelt, MD have performed their senior research projects at the Howard University Beltsville Research Campus in Beltsville, MD. The projects have focused generally on the testing and correction of low-cost sensors and development of instrumentation for use in profiling the lower atmosphere. Specifically, we have developed a low-cost tethersonde system and used it to carry aloft a low-cost instrument that measures particulate matter (PM) as well as a standard radiosonde measuring temperature, pressure and relative humidity. The low-cost PM sensor was found to provide artificially high values of PM under conditions of elevated relative humidity likely due to the presence of hygroscopic aerosols. Reference measurements of PM were used to develop a correction technique for the low-cost PM sensor. Profiling measurements of temperature and PM during the breakdown of a nocturnal inversion were performed using the tethersonde system on August 30, 2019. The evolution of temperature during the breakdown of the inversion was studied and compared with model forecasts. The attempt to measure PM during the tethersonde experiment was not successful due, we believe, to the packaging of the low cost sensor. Future cohorts of students from Eleanor Roosevelt High School students will work on improving the instrumentation and measurements shown here as we continue the collaboration between the Howard University Beltsville Campus and the local school system.

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Axel Lauer, Lisa Bock, Birgit Hassler, Marc Schröder, and Martin Stengel

Abstract

Simulating clouds with global climate models is challenging as relevant physics involves many non-linear processes covering a wide range of spatial and temporal scales. As key components of the hydrological cycle and the climate system, an evaluation of clouds from models used for climate projections is an important prerequisite for assessing the confidence in the results from these models. Here, we compare output from models contributing to Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with satellite data and with results from their predecessors (CMIP5). We use multi-product reference datasets to estimate the observational uncertainties associated with different sensors and with internal variability on a per-pixel basis. Selected cloud properties are also analyzed by region and by dynamical regime and thermodynamic conditions.

Our results show that for parameters such as total cloud cover, cloud water path and cloud radiative effect, the CMIP6 multi-model mean performs slightly better than the CMIP5 ensemble mean in terms of mean bias, pattern correlation and relative root-mean square deviation. The inter-model spread in CMIP6, however, is not reduced compared to CMIP5. Compared with CALIPSO-ICECLOUD data, the CMIP5/6 models overestimate cloud ice particularly in the lower and middle troposphere partly due to too high ice fractions for given temperatures. This bias is reduced in the CMIP6 multi-model mean. While many known biases such as an underestimation in cloud cover in stratocumulus regions remain in CMIP6, we find that the CMIP5 problem of too few but too reflective clouds over the Southern Ocean is significantly improved.

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Ophélia Miralles, Daniel Steinfield, Olivia Martius, and Anthony C. Davison

Abstract

Near-surface wind is difficult to estimate using global numerical weather and climate models, as airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach based on deep learning and a high-resolution Digital Elevation Model to spatially downscale hourly near-surface wind fields at coarse resolution from ERA5 reanalysis from their original 25 km to a 1.1 km grid. A 1.1 km resolution wind dataset for 2016–2020 from the operational numerical weather prediction model COSMO-1 of the national weather service, MeteoSwiss, is used to train and validate our model, a generative adversarial network (GAN) with gradient penalized Wasserstein loss aided by transfer learning. The results are realistic-looking high-resolution historical maps of gridded hourly wind fields over Switzerland and very good and robust predictions of the aggregated wind speed distribution. Regionally averaged image-specific metrics show a clear improvement in prediction compared to ERA5, with skill measures generally better for locations over the flatter Swiss Plateau than for Alpine regions. The downscaled wind fields demonstrate higher-resolution, physically plausible orographic effects, such as ridge acceleration and sheltering, which are not resolved in the original ERA5 fields.

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Douglas R. Allen, Daniel Hodyss, Karl W. Hoppel, and Gerald E. Nedoluha

Abstract

An essential component of four-dimensional variational data assimilation is the tangent linear model (TLM), which is a linearized version of the full nonlinear forecast model. A relatively new approach to calculating the TLM is a regression model called the Ensemble Tangent Linear Model (ETLM). Here we validate the ETLM for linearizing a nonorographic gravity wave drag (NGWD) sub-grid scale model. The regression is applied to an ensemble created by perturbing the atmospheric state and calculating one time step of the NGWD model. The ETLM is validated using independent perturbations based on archived analysis increments. We examine how the skill of the NGWD ETLM depends on the choice of ensemble perturbation, ensemble size, amount of ensemble inflation/deflation, and the size of the localization stencil. After examining the nearly perfect results using a large ETLM ensemble (100,000 members), optimal tuning is then performed for 150 - 500 members. For smaller ETLM ensembles, spurious noise due to sampling error could be reduced either by downscaling the perturbations or by localizing the ETLM. The impact of localization decreases as the ETLM ensemble size increases. We then validate the ETLMs using one year of archived DA analysis increments. The skill varies over time with percentage errors relative to persistence forecasts (where 100% is no skill, 0% is a perfect forecast) generally ranging from ~50-90% (~40-80%) for ETLMs with 150 (500) members. The ETLM is also shown to propagate small increments (1% of the size of analysis increments) with fractional errors of ~10%.

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Andrea E. Gordon, Steven M. Cavallo, and Amanda K. Novak

Abstract

Tropopause Polar Vortices (TPVs) are coherent circulations that occur over polar regions and can be identified by a local minimum in potential temperature and local maximum in potential vorticity. Numerous studies have focused on TPVs in the Arctic region, however, no previous studies have focused on the Antarctic. Given the role of TPVs in the Northern Hemisphere with surface cyclones and other extreme weather, and the role that surface cyclones can play on moisture transport and sea ice breakup, it is important to understand whether similar associations exist in the Southern Hemisphere. Here, characteristics of TPVs in the Antarctic are evaluated for the first time under the hypothesis that their characteristics do not significantly differ from those of the Northern Hemisphere. To improve understanding of Antarctic TPV characteristics, this study examines TPVs of the Southern Hemisphere and compares them to their Northern Hemisphere counterparts from 1979-2018 using ERA-Interim data. Common characteristics of TPVs including frequency, locations, lifetimes, strength, and seasonality are evaluated. Results indicate that topography correlates to the geographic distribution of TPVs and the locations of local maxima TPV occurrence, as observed in the Northern Hemisphere. Additionally, TPVs in the Southern Hemisphere exhibit seasonal variations for amplitude, lifetime, and minimum potential temperature. Southern Hemisphere TPVs share many similar characteristics to those observed in the Northern Hemisphere, including longer summer lifetimes.The association of Southern Hemisphere TPVs and surface cyclone frequency is explored, and it appears that TPVs have a precursory role to surface cyclones, as seen in the Northern Hemisphere.

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John R. Albers, Matthew Newman, Andrew Hoell, Melissa L. Breeden, Yan Wang, and Jiale Lou

Abstract

The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning-based prediction model called a Linear Inverse Model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden-Julian Oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20-30 years.

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Walker S. Ashley, Alex M. Haberlie, and Vittorio A. Gensini

Abstract

A supercell is a distinct type of intense, long-lived thunderstorm that is defined by its quasi-steady, rotating updraft. Supercells are responsible for most damaging hail and deadly tornadoes, causing billions of dollars in losses and hundreds of casualties annually. This research uses high-resolution, convection-permitting climate simulations across 15-yr epochs that span the 21st century to assess how supercells may change across the United States. Specifically, the study explores how late 20th century supercell populations compare with their late 21st century counterparts for two—intermediate and pessimistic—anthropogenic climate change trajectories. An algorithm identifies, segments, and tracks supercells in the simulation output using updraft helicity, which measures the magnitude of corkscrew flow through a storm’s updraft and is a common proxy for supercells. Results reveal that supercells will be more frequent and intense in future climates, with robust spatiotemporal shifts in their populations. Supercells are projected to become more numerous in regions of the eastern United States, while decreasing in frequency in portions of the Great Plains. Supercell risk is expected to escalate outside of the traditional severe storm season, with supercells and their perils likely to increase in late winter and early spring months under both emissions scenarios. Conversely, the latter part of the severe storm season may be curtailed, with supercells expected to decrease midsummer through early fall. These results suggest the potential for more significant tornadoes, hail, and extreme rainfall that, when combined with an increasingly vulnerable society, may produce disastrous consequences.

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P. Chambon, J.-F. Mahfouf, O. Audouin, C. Birman, N. Fourrié, C. Loo, M. Martet, P. Moll, C. Payan, V. Pourret, and D. Raspaud

Abstract

Observing System Experiments were undertaken within the 4D-Var data assimilation of the Météo-France global Numerical Weather Prediction (NWP) model. A six-month period was chosen (October 2019 - March 2020) where 40 millions of observations per day were assimilated. The importance of in-situ observations provided by aircraft, radiosondes and surface weather stations, despite their small fractional amount (7 %), has been confirmed particularly in the Northern Hemisphere. Moreover, the largest impact over Europe in terms of Root Mean Square Error (RMSE) scores comes from surface observations. Satellite data play a dominant role over tropical regions and the Southern Hemisphere. Microwave radiances have a more pronounced impact on the long range and on the humidity field than infrared radiances, despite being less numerous (10 % versus 80 %). Bending angles impact significantly the quality of the upper troposphere / lower stratosphere temperature of the tropics and Southern Hemisphere. Atmospheric Motion Vectors (AMVs) are beneficial in wind forecasts at low and high levels in the tropics and the Southern Hemisphere, but also in the humidity field. Such impacts are only significant during the first 48 hours of the forecasts. Scatterometer winds have an impact restricted to low levels which is kept at longer ranges. A comparison with Forecast Sensitivity - Observation Impact studies over a 3 month period using the same measure of short-range (24 h) forecast errors reveals that the ranking between the major observing systems is kept between these two ways of measuring observation impact in NWP. From our conclusions, recommendations are provided on possible evolutions of the global observing system for NWP.

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