Han, R., Wang, H., Hu, Z. - Z., Kumar, A., Li, W., Long, L. N., et al. (2016). An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO.
J. Climate, 29(18), 6401–6423.
Hu, Z. - Z., Huang, B., Kinter, J. L., Wu, Z., & Kumar, A. (2012). Connection of the stratospheric QBO with global atmospheric general circulation and tropical SST. Part II: interdecadal variations.
Clim Dyn, 38(1-2), 25–43.
Huang, B., Hu, Z. - Z., Kinter, J. L., Wu, Z., & Kumar, A. (2012). Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: methodology and composite life cycle.
Clim Dyn, 38(1-2), 1–23.
Kim, D., Lee, S. - K., Lopez, H., Foltz, G. R., Misra, V., & Kumar, A. (2020). On the Role of Pacific-Atlantic SST Contrast and Associated Caribbean Sea Convection in August-October U.S. Regional Rainfall Variability.
Geophys. Res. Lett., 47(11).
Abstract: This study investigates the large‐scale atmospheric processes that lead to U.S. precipitation variability in late summer to midfall (August–October; ASO) and shows that the well‐recognized relationship between North Atlantic Subtropical High and U.S. precipitation in peak summer (June–August) significantly weakens in ASO. The working hypothesis derived from our analysis is that in ASO convective activity in the Caribbean Sea, modulated by the tropical Pacific‐Atlantic sea surface temperature (SST) anomaly contrast, directly influences the North American Low‐Level Jet and thus U.S. precipitation east of the Rockies, through a Gill‐type response. This hypothesis derived from observations is strongly supported by a long‐term climate model simulation and by a linear baroclinic atmospheric model with prescribed diabatic forcings in the Caribbean Sea. This study integrates key findings from previous studies and advances a consistent physical rationale that links the Pacific‐Atlantic SST anomaly contrast, Caribbean Sea convective activity, and U.S. rainfall in ASO.
Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti, A., et al. (2019). Process-Oriented Evaluation of Climate and Weather Forecasting Models.
Bull. Amer. Meteor. Soc., 100(9), 1665–1686.
Abstract: Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers' model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.
Phelps, M., Kumar, A., & O'Brien, J. J. (2002).
Potential predictability in the NCEP/CPC dynamical seasonal forecast system. COAPS Technical Report 02-04a. Tallahassee, FL: Center for Ocean-Atmospheric Prediction Studies, Florida State University.
Phelps, M. W., Kumar, A., & O'Brien, J. J. (2004). Potential Predictability in the NCEP CPC Dynamical Seasonal Forecast System.
J. Climate, 17(19), 3775–3785.
Shaevitz, D. A., Camargo, S. J., Sobel, A. H., Jonas, J. A., Kim, D., Kumar, A., et al. (2014). Characteristics of tropical cyclones in high-resolution models in the present climate.
J. Adv. Model. Earth Syst., 6(4), 1154–1172.
Wang, H., Long, L., Kumar, A., Wang, W., Schemm, J. - K. E., Zhao, M., et al. (2014). How Well Do Global Climate Models Simulate the Variability of Atlantic Tropical Cyclones Associated with ENSO?
J. Climate, 27(15), 5673–5692.