Feed aggregator

Abstract

Accurate estimation and attribution of large-scale changes in plant transpiration are critical to understand the impacts of vegetation dynamics on the terrestrial hydrological cycle. However, these aspects remain poorly understood due to the limited reliability of global transpiration products. Here we compile data from 101 site-based transpiration measurements across the globe and use them to constrain three biophysically based data-driven transpiration products. The constrained transpiration reveals a prominent increasing trend of 0.61–0.79 mm yr−2 during 1980–2021, which is overestimated by 8%–32% in unconstrained transpiration. We further find that the global transpiration increase is mainly driven by leaf area index increase (40%), followed by climate change (19%), though offset partly by CO2-induced stomatal closure (−38%) and land use and cover change (−3%). Our refined estimates indicate a less substantial increase of global transpiration than previously thought, improving the understanding of transpiration change impact on global hydrological cycle.

Abstract

Aerosol-cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.

Abstract

Bedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady-state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi-stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform-group turnover timescales are the key autogenic timescales of bed evolution that set the transition time-periods between different noise regimes in bedform evolution. Results suggest that bedform-group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods.

Abstract

We quantify the effect of dust accumulation at Jezero crater by means of a Dust Correction Factor (DCF) for the solar radiation measured by the photodiodes of the Radiation and Dust Sensor of the Mars 2020 mission. After one Mars Year, dust on the photodiode surface attenuated 25%–30% of the incoming solar radiation. The DCF did not decrease monotonically; we use a model to reproduce its evolution and to derive dust deposition and lifting rates, showing that dust removal is 9 times larger at Jezero crater than at InSight's location in western Elysium Planitia. The model fit obtained using observed opacities is further improved when fed with dust sedimentation rates simulated by a GCM that considers a particle size distrtibution. Projections show seasonal net dust removal, being encouraging for the long-term survival of solar-powered missions to Jezero or similarly active dust lifting regions.

Abstract

Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i-GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i-GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i-GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model-simulated forced SAT trend patterns.

Abstract

Recent distributed acoustic sensing (DAS) experiments in ocean areas throughout the world have accumulated records for various wavefields. However, there are few tsunami records because tsunami observation depends on the DAS experimental period and its location. From continuous DAS records, we found tsunami signals at a frequency band of 5–30 mHz, which correspond to high-frequency components of tsunamis and their propagation velocities differ from low-frequency tsunamis. We estimated time series of the tsunami excitations at the source using the DAS records, which are consistent with those using records of ocean-bottom absolute pressure gauges. Our study suggests that DAS records can be used for detecting tsunami propagations in the regions where other geophysical instruments are not available, and contribute to elucidating their excitation mechanisms.

Science, Volume 385, Issue 6704, Page 53-56, July 2024.

Science, Volume 385, Issue 6704, Page 57-62, July 2024.

Science, Volume 384, Issue 6703, Page 1435-1440, June 2024.

Science, Volume 384, Issue 6700, Page 1074-1074, June 2024.

Science, Volume 384, Issue 6700, Page 1075-1075, June 2024.

Science, Volume 384, Issue 6700, Page 1111-1117, June 2024.

Science, Volume 384, Issue 6700, Page 1126-1134, June 2024.

Science, Volume 384, Issue 6700, Page 1134-1142, June 2024.

Science, Volume 384, Issue 6700, Page 1091-1095, June 2024.

Science, Volume 384, Issue 6700, Page 1100-1104, June 2024.

Science, Volume 384, Issue 6700, Page 1122-1126, June 2024.

Science, Volume 384, Issue 6700, Page 1105-1110, June 2024.

Science, Volume 384, Issue 6700, Page 1096-1099, June 2024.

Science, Volume 384, Issue 6700, Page 1086-1090, June 2024.