·         Pattern and time-scale dependencies of temperature-precipitation correlations in the Northern Hemisphere extra-tropics 

2021 ◽  
Author(s):  
Ulrike Herzschuh ◽  
Thomas Böhmer ◽  
Xianyong Cao ◽  
Raphael Herbert ◽  
Anne Dallmeyer ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Time Scale ◽  
Climate Models ◽  
Negative Temperature ◽  
Temporal Variations ◽  
The Holocene ◽  
Latitudinal Dependence ◽  
Long Time ◽  
Positive Correlations ◽  
Long Timescales

<p>Future precipitation levels under a warming climate remain uncertain because current climate models have largely failed to reproduce observed variations in temperature-precipitation correlations. Our analyses of Holocene proxy-based temperature-precipitation correlations from 1647 Northern Hemisphere extratropical pollen records reveal a significant latitudinal dependence, temporal variations between the early, middle, and late Holocene, and differences between short and long timescales. These proxy-based variations are largely consistent with patterns obtained from transient climate simulations for the Holocene. Temperature-precipitation correlations increase from short to long time-scales. While high latitudes and subtropical monsoon areas show mainly stable positive correlations throughout the Holocene, the mid-latitude pattern is temporally and spatially more variable. In particular, we identified a reversal to negative temperature-precipitation correlations in the eastern North American and European mid-latitudes during the mid-Holocene that mainly related to slowed down westerlies and a switch to moisture-limited convection under a warm climate. We conclude that the effect of climate change on land areas is more complex than the commonly assumed “wetter climate in a warmer world”. Future predictions need to consider that warming related precipitation change is time-scale dependent.</p>

scholarly journals Temporal variations in rockfall and rock-wall retreat rates in a deglaciated valley over the past 11 k.y.

Geology ◽  
2020 ◽  
Vol 48 (6) ◽  
pp. 594-598 ◽  
Author(s):  
Solmaz Mohadjer ◽  
Todd A. Ehlers ◽  
Matthias Nettesheim ◽  
Marco B. Ott ◽  
Christoph Glotzbach ◽  
...  
Keyword(s):  
Power Law ◽  
Time Scale ◽  
Temporal Variations ◽  
Climatic Conditions ◽  
Short Time Scale ◽  
Similar Frequency ◽  
Rock Wall ◽  
Significant Difference ◽  
Long Time ◽  
Frequency Magnitude

Abstract This study addresses the temporal variations in rockfall activity in the 5.2 km2 calcareous cliffs of the deglaciated Lauterbrunnen Valley, Switzerland. We did this using 19 campaigns of repeated terrestrial laser scans (TLS) over 5.2 yr, power-law predicted behavior from extrapolation of the TLS-derived frequency-magnitude relationship, and estimates of long-time-scale (∼11 k.y.) activity based on the volume of preserved postglacial rockfall talus. Results from the short-time-scale observations indicate no statistically significant difference between TLS observations averaging over 1.5 versus 5.2 yr. Rock-wall retreat rates in both cases are 0.03–0.08 mm/yr. In contrast, the power-law predicted rock-wall retreat rates are 0.14–0.22 mm/yr, and long-term rates from talus volumes are 0.27–0.38 mm/yr. These results suggest (1) short (1.5 yr) TLS inventories of rockfalls provide (within uncertainties) similar frequency-magnitude relationships as longer (5.2 yr) inventories, thereby suggesting short observation periods may be sufficient for hazard characterization from TLS, and (2) higher rock-wall retreat rates over long time scales (Holocene averaged) may reflect debuttressing and stress relaxation effects after glacial retreat, and/or enhanced rockfall activity under periglacial (climatic) conditions.

Northern Hemisphere temperature to precipitation relationships during the last Glacial from pollen records and climate simulations

2020 ◽  
Author(s):  
Anna Sommani ◽  
Nils Weitzel ◽  
Kira Rehfeld
Keyword(s):  
Northern Hemisphere ◽  
Negative Correlation ◽  
Climate Models ◽  
Seasonal Rainfall ◽  
The Holocene ◽  
Last Glacial ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Pollen Records ◽  
The Last Glacial

<p>The hydrological response to radiative forcing is less understood than the thermal one: many climate models have difficulties in simulating seasonal rainfall and its variability. Indeed, future precipitation projections are much more uncertain than those of temperature. However, confident projections of precipitation are of crucial importance, particularly for highly populated regions where agriculture strongly relies on seasonal rainfall, such as South and Central Asia.</p><p>Instrumental data from Eurasia show a negative correlation between temperature and precipitation on short timescales (10<sup>-3</sup> to 10<sup>0</sup> years). However, on longer timescales (10<sup>1</sup> to 10<sup>3</sup> years), proxy data covering the Holocene show a positive correlation between temperature and precipitation. Climate models in contrast simulate a negative correlation on all timescales. To extend previous estimates to longer time scales, we focus on the last Glacial period, characterized by colder temperature than the Holocene as well as pronounced millennial-scale climate fluctuations in the Northern Hemisphere.</p><p>We reconstruct temperature and precipitation from four high resolution pollen records at mid-latitudes in the Northern Hemisphere. The estimates are compared with climate simulations. The chosen proxy sites cover the East and West coasts of both the Eurasian and North American continent. We employ four different statistical reconstruction methods to assess validity and biases of each method. The differences between reconstructed and simulated temperature-precipitation relationships as well as the zonal structure of orbital- and millennial-scale variations are examined. In particular, we explore the thermodynamic and dynamic contributions to the inferred relationships between temperature and precipitation.</p>

scholarly journals Seasonal and Spatial Variability of Near-Inertial Kinetic Energy from Historical Moored Velocity Records

2007 ◽  
Vol 37 (8) ◽  
pp. 2022-2037 ◽  
Author(s):  
Matthew H. Alford ◽  
Maya Whitmont
Keyword(s):  
Kinetic Energy ◽  
Northern Hemisphere ◽  
Time Scale ◽  
Seasonal Cycle ◽  
Ocean Basin ◽  
Buoyancy Frequency ◽  
Latitudinal Dependence ◽  
Spatial Coverage ◽  
Globally Distributed ◽  
Western Portion

Abstract Temporal and spatial patterns of near-inertial kinetic energy (KEmoor) are investigated in a database of 2480 globally distributed, moored current-meter records (deployed on 690 separate moorings) and compared with the distribution of wind-forced mixed-layer energy flux FML. By computing KEmoor using short (30 day) multitaper spectral windows, the seasonal cycle is resolved. Clear winter enhancement by a factor of 4–5 is seen in the Northern Hemisphere for latitudes 25°–45° at all depths <4500 m, in close agreement with the magnitude, phase, and latitudinal dependence of the seasonal cycle of FML. In the Southern Hemisphere, data coverage is poorer, but a weaker seasonal cycle (a factor of 2) in both KEmoor and FML is still resolvable between 35° and 50°. When Wentzel–Kramers–Brillouin (WKB) scaled using climatological buoyancy-frequency profiles, summer KEmoor is approximately constant in depth while showing a clear decrease by a factor of 4–5 from 500 to 3500 m in winter. Spatial coverage is sufficient in the Northern Hemisphere to resolve broad KEmoor maxima in the western portion of each ocean basin in winter, generally collocated with FML maxima associated with storm forcing. The ratio of depth-integrated KEmoor to FML gives a replenishment time scale, which is about 10 days in midlatitudes, consistent with 1) previous estimates of the dissipation time scale of the internal wave continuum and 2) the presence of a seasonal cycle. Its increase to ≈70–80 days at lower latitudes is a possible signature of equatorward propagation of near-inertial waves. The seasonal modulation of the magnitude of KEmoor, its similarity to that in FML, and the depth decay and western intensification in winter but not in summer are consistent with a primarily wind-forced near-inertial field for latitudes poleward of ≈25°.

Switches of Holocene temperature-precipitation correlations in northern Hemisphere extra-tropics comparing proxy and model data

2020 ◽  
Author(s):  
Ulrike Herzschuh ◽  
Thomas Boehmer ◽  
Raphael Herbert ◽  
Thomas Laepple ◽  
Richard Telford ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
General Circulation ◽  
Climate Models ◽  
General Circulation Models ◽  
Global Scale ◽  
Positive Temperature ◽  
Climate Simulations ◽  
Precipitation Response ◽  
Long Time ◽  
Background Temperature

<p>Switches of temperature-precipitation correlation in northern Hemisphere extra-tropics</p><p>Future precipitation response to warming remains uncertain because climate models poorly reproduce observed changes of temperature-precipitation correlations. However, restricting model validations to the observational period may yield to misleading conclusions due to the complexity of the involved processes. Our analyses of Holocene proxy-based temperature-precipitation correlations from 1500 northern Hemisphere extratropic pollen records portrayed significant latitudinal dependance, temporal changes from the early to late Holocene as well as differences between short and long time-scales. These observed variations were found to be mostly consistent with patterns simulated by Holocene transient climate simulations. Our results suggest that the strength of positive temperature-precipitation correlations in high-latitudes is sensitive to the background temperature while monsoonal subtropics reflect spatial shifts of circulation systems; and correlation sign switches in mid-latitudes relate to changes of westerlies strength. We conclude that regional and continental climate change on land is more complex than the expected “wetter climate in a warmer world” assumption which holds well at the global scale. On the other hand, long-term projections of precipitation may be better than previously thought as major processes seem to be already implemented correctly in general circulation models.</p>

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Long-Time-Scale Magnetization Ordering Induced by an Adsorbed Chiral Monolayer on Ferromagnets

ACS Nano ◽  
2021 ◽  
Author(s):  
I. Meirzada ◽  
N. Sukenik ◽  
G. Haim ◽  
S. Yochelis ◽  
L. T. Baczewski ◽  
...  
Keyword(s):  
Time Scale ◽  
Long Time
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