Use of wild edible plants: A comparative analysis of seven plant species for potential food applications - Ehsan Ali; Muhammad Farooq Azhar (Food and Humanity, 2026)

Improving subseasonal Indian summer monsoon rainfall forecasts with U-Net calibration - Emile D. Esmaili; Andrew W. Robertson; Muhammad Azhar Ehsan; Bohar Singh; Aaron Kaplan; Avijit Dey; Susmitha Joseph (Climate Dynamics, 2026)

Assessment of urban and peri-urban grazing practices from herders and local community perspective in the semiarid region of Pakistan - Muhammad Farooq Azhar; Ghulam Yasin; Asghar Ali; Abida Aziz; Ghulam Yasin; Daniyal Hussain; Ehsan Ali (Environmental Monitoring and Assessment, 2026)

ENSO teleconnections and predictability of the boreal summer temperature over the Arabian Peninsula in C3S and Saudi-KAU seasonal forecast systems - Mansour Almazroui; M. Salman Khalid; Muhammad Adnan Abid; Irfan Ur Rashid; Shahzad Kamil; Haroon Siddiqui; M. Nazrul Islam; Muhammad Ismail; Muhammad Azhar Ehsan; Enda O'Brien; Mazen Asiri; Rayees Ahmed; et al. (Atmospheric Research, 2025)

Evaluation of drought hazards and coping strategies adopted by pastoral communities in the Cholistan Rangeland of Pakistan - Muhammad Farooq Azhar; Muhammad Junaid Faiz; Ehsan Ali; Abida Aziz; Muhammad Akbar; Ghulam Raza; Muhammad Abdullah; Muhammad Muzammil Habib; Muhammad Farooq Akram (Environmental Development, 2024)

Calibrated probabilistic sub-seasonal forecasting for Pakistan’s monsoon rainfall in 2022 - Bohar Singh; Muhammad Azhar Ehsan; Andrew W. Robertson (Climate Dynamics, 2024)

Forecast skill of Bangladesh summer monsoon rainfall in C3S and NMME models after calibration - Muhammad Azhar Ehsan; Bohar Singh (Dynamics of Atmospheres and Oceans, 2023)

Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5) - Raju Attada; Muhammad Azhar Ehsan; Prasanth A. Pillai (Pure and Applied Geophysics, 2022)

Seasonal predictability of Ethiopian Kiremt rainfall and forecast skill of ECMWF's SEAS5 model - Muhammad Azhar Ehsan; Michael K. Tippett; Andrew W. Robertson; Mansour Almazroui; Muhammad Ismail; Tufa Dinku; Nachiketa Acharya; Asher Siebert; Jemal Seid Ahmed; Asaminew Teshome (Climate Dynamics, 2021)

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble - Muhammad Azhar Ehsan; Michael K. Tippett; Fred Kucharski; Mansour Almazroui; Muhammad Ismail (International Journal of Climatology, 2020)
The potential and actual forecast skill of peak summer monsoon precipitation (July–August [JA]) over the core summer monsoon region of Pakistan (CSMRP: 68°–76°E, 30°–36°N) is investigated. The predictions from the latest version of the European Centre for Medium‐Range Weather Forecast System5 (SEAS5) and the North American Multimodel Ensemble (NMME) initialized in the beginning of June, May, and April are utilized. The potential skill is estimated by signal‐to‐noise ratio and perfect model correlation. The forecast skill is evaluated using the temporal anomaly correlation as well as the ranked probability skill score for probabilistic forecasts. The SEAS5 generally captures the observed climatological mean and variability patterns of peak summer monsoon precipitation over the region, with some biases mainly located over the complex topography zones. Observed SST anomalies in the El Niño–Southern Oscillation (ENSO) region are negatively correlated with observed peak summer monsoon precipitation over the region. The ENSO–precipitation connection is weakly reproduced in SEAS5 at Lead‐1 and fades gradually with increasing lead time. The potentially predictable component (signal) is small compared to the unpredictable part (noise). Nonetheless, statistically significant potential skill is found over the CSMRP. However, the forecast skill of peak summer monsoon precipitation over the region is lower than the potential skill in SEAS5. A multimodel ensemble average of five models shows little benefit in predicting peak monsoon precipitation over Pakistan. Extended spatial averaging has some impact on the skill; however, temporal averaging has no effect on the skill of the summer monsoon precipitation in SEAS5. Together these results show limitations of the current seasonal prediction models and demonstrate their inadequate performance in predicting peak summer monsoon precipitation over Pakistan.

Potential predictability of boreal winter precipitation over central-southwest Asia in the North American multi-model ensemble - Muhammad Azhar Ehsan; Fred Kucharski; Mansour Almazroui (Climate Dynamics, 2020)

Potential predictability of Arabian peninsula summer surface air temperature in the North American multimodel ensemble - Muhammad Azhar Ehsan; Fred Kucharski; Mansour Almazroui; Muhammad Ismail; Michael K. Tippett (Climate Dynamics, 2019)

Sensitivity of AGCM ‐simulated regional JJAS precipitation to different convective parameterization schemes - Muhammad Azhar Ehsan; Mansour Almazroui; Ahmed Yousef; O'Brien Enda; Michael K. Tippett; Fred Kucharski; Abdulrahman A. Alkhalaf (International Journal of Climatology, 2017)
This study examines the effects of convective parameterization schemes ( CPSs ) on the simulated June–September ( JJAS ) precipitation climatology, variability and predictability over southwestern Arabian Peninsula and northeast Africa during 1981–2014 within an atmospheric global climate model framework. The three CPSs used are: the simplified Arakawa–Schubert ( SAS ) scheme; the SAS scheme with Tokioka modification ( STOK ) and the Emanuel ( EMAN ) scheme coupled with a probability distribution function‐based cloud parameterization scheme. SAS and STOK overestimate JJAS total precipitation over the selected domain compared to observations, while EMAN underestimates. EMAN provides the most realistic distribution of precipitation and spatial distribution of convective precipitation despite the tendency for underestimating the total precipitation. The principal patterns of precipitation variability (as measured by the leading empirical orthogonal functions) in EMAN and STOK are clearly related to the observed pattern, while the maximum variability in SAS occurs in a completely different location. The El Niño Southern Oscillation‐related JJAS precipitation teleconnection simulated by the SAS scheme is very weak as compared to observations and other two CPSs . The signal‐to‐noise ratio estimated by SAS and STOK is very low as compared to EMAN . Correlation analysis shows that EMAN performs better than the other two CPSs . Low (high) outgoing longwave radiation ( OLR ) values are indicative of enhanced (suppressed) convection and hence more (less) cloud coverage. The root mean square error estimated for OLR in EMAN is lower than that of the other two CPSs . The vertical structure of specific humidity and temperature shows less error in EMAN than that of the other two CPSs , which could be a reason for better predictability over the region of interest.

Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation - Muhammad Azhar Ehsan; Michael K. Tippett; Mansour Almazroui; Muhammad Ismail; Ahmed Yousef; Fred Kucharski; Mohamed Omar; Mahmoud Hussein; Abdulrahman A. Alkhalaf (Climate Dynamics, 2017)