More Like this

1. Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Analytical Modeling to Identify the Mechanisms for CMIP Ensemble Error Spread

   https://doi.org/10.1175/JCLI-D-22-0255.1  

   Sun, Chao; Liang, Xin-Zhong (April 2023, Journal of Climate)

   Abstract Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases. 

   more » « less
2. Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions

   https://doi.org/10.1007/s00382-023-07082-1  

   Qin, Yi; Tang, Qi; Xue, Yongkang; Liu, Ye; Lin, Yanluan (April 2024, Climate Dynamics)

   Abstract Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction. 

   more » « less
3. Observed Relationships between Sea Surface Temperature, Vertical Wind Shear, Tropical Organized Deep Convection, and Radiative Effects

   https://doi.org/10.1175/JCLI-D-23-0262.1  

   Hsiao, Wei-Ting; Maloney, Eric D; Leitmann-Niimi, Nicolas M; Kummerow, Christian D (February 2024, Journal of Climate)

   Abstract Organized deep convective activity has been routinely monitored by satellite precipitation radar from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM). Organized deep convective activity is found to increase not only with sea surface temperature (SST) above 27°C, but also with low-level wind shear. Precipitation shows a similar increasing relationship with both SST and low-level wind shear, except for the highest low-level wind shear. These observations suggest that the threshold for organized deep convection and precipitation in the tropics should consider not only SST, but also vertical wind shear. The longwave cloud radiative feedback, measured as the tropospheric longwave cloud radiative heating per amount of precipitation, is found to generally increase with stronger organized deep convective activity as SST and low-level wind shear increase. Organized deep convective activity, the longwave cloud radiative feedback, and cirrus ice cloud cover per amount of precipitation also appear to be controlled more strongly by SST than by the deviation of SST from its tropical mean. This study hints at the importance of non-thermodynamic factors such as vertical wind shear for impacting tropical convective structure, cloud properties, and associated radiative energy budget of the tropics. Significance StatementThis study uses tropical satellite observations to demonstrate that vertical wind shear affects the relationship between sea surface temperature and tropical organized deep convection and precipitation. Shear also affects associated cloud properties and how clouds affect the flow of radiation in the atmosphere. Although how vertical wind shear affects convective organization has long been studied in the mesoscale community, the study attempts to apply mesoscale theory to explain the large-scale mean organization of tropical deep convection, cloud properties, and radiative feedbacks. The study also provides a quantitative observational baseline of how vertical wind shear modifies cloud radiative effects and convective organization, which can be compared to numerical simulations. 

   more » « less
4. Thermally Direct Mesoscale Circulations Caused by Land Surface Roughness Anomalies

   https://doi.org/10.1029/2023GL105150  

   Cheng, Yu; McColl, Kaighin A. (August 2023, Geophysical Research Letters)

   Deforestation, urbanization and construction of wind farms can change the land surface roughness, which can further influence surface heat fluxes and thus weather and climate. Land surface roughness anomalies can dynamically trigger convergence through changing mean wind speed. Here, we report a new mechanism, in which roughness anomalies cause thermally direct mesoscale circulations and anomalous precipitation. To study this mechanism, we conduct cloud‐permitting simulations over an idealized land surface with prescribed surface roughness anomalies. Anomalously high roughness increases turbulent mixing near the surface, which decreases land surface temperature and outgoing longwave radiation. The additional surface net radiation partly goes into greater sensible heat flux, which triggers mesoscale circulations driven by differential heating. As a result, precipitation over the high‐roughness anomaly is generally larger than that over the low‐roughness background. This new mechanism, not present in climate models, may be relevant to storm formation over wind farms, cities and forests. 

   more » « less
5. Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods

   https://doi.org/10.1175/JTECH-D-21-0084.1  

   Haynes, John M; Noh, Yoo-Jeong; Miller, Steven D; Haynes, Katherine D; Ebert-Uphoff, Imme; Heidinger, Andrew (March 2022, Journal of Atmospheric and Oceanic Technology)

   Abstract The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning–based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The random forest (RF) and neural network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively sensed cloud boundaries from the radar and lidar systems on board theCloudSatandCALIPSOsatellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors. Significance StatementUsing satellites to detect the heights of clouds in the atmosphere is important for a variety of weather applications, including aviation weather forecasting. However, detecting low clouds can be challenging if there are other clouds above them. To address this, we have developed machine learning–based models that can be used with passive satellite instruments. These models use satellite observations at visible and infrared wavelengths, an estimate of relative humidity in the atmosphere, and geographic and surface-type information to predict whether low clouds are present. Our results show that these models have significant skill at predicting low clouds, even in the presence of higher cloud layers. 

   more » « less