The influence of vegetation on the hydrologic cycle in a global climate model

Author(s):  
D. Rind
2017 ◽  
Vol 30 (24) ◽  
pp. 9999-10017 ◽  
Author(s):  
Hansi K. A. Singh ◽  
Cecilia M. Bitz ◽  
Aaron Donohoe ◽  
Philip J. Rasch

Numerical water tracers implemented in a global climate model are used to study how polar hydroclimate responds to CO2-induced warming from a source–receptor perspective. Although remote moisture sources contribute substantially more to polar precipitation year-round in the mean state, an increase in locally sourced moisture is crucial to the winter season polar precipitation response to greenhouse gas forcing. In general, the polar hydroclimate response to CO2-induced warming is strongly seasonal: over both the Arctic and Antarctic, locally sourced moisture constitutes a larger fraction of the precipitation in winter, while remote sources become even more dominant in summer. Increased local evaporation in fall and winter is coincident with sea ice retreat, which greatly augments local moisture sources in these seasons. In summer, however, larger contributions from more remote moisture source regions are consistent with an increase in moisture residence times and a longer moisture transport length scale, which produces a robust hydrologic cycle response to CO2-induced warming globally. The critical role of locally sourced moisture in the hydrologic cycle response of both the Arctic and Antarctic is distinct from controlling factors elsewhere on the globe; for this reason, great care should be taken in interpreting polar isotopic proxy records from climate states unlike the present.


2006 ◽  
Vol 7 (5) ◽  
pp. 857-867 ◽  
Author(s):  
Paul A. Dirmeyer

Abstract The impact of improvements in land surface initialization and specification of observed rainfall in global climate model simulations of boreal summer are examined to determine how the changes propagate around the hydrologic cycle in the coupled land–atmosphere system. On the global scale, about 70% of any imparted signal in the hydrologic cycle is lost in the transition from atmosphere to land, and 70% of the remaining signal is lost from land to atmosphere. This means that globally, less than 10% of the signal of any change survives the complete circuit of the hydrologic cycle in this model. Regionally, there is a great deal of variability. Specification of observed precipitation to the land component of the climate model strongly communicates its signal to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. A maximum in signal transmission to model precipitation exists in between, peaking where mean rainfall rates are 1.5–2 mm day−1. It appears that the nature of the climate system inherently limits to these regions the potential impact on prediction of improvements in the ability of models to simulate the water cycle. Land initial conditions impart a weaker signal on the system than replacement of precipitation, so a weaker response is realized in the system, focused mainly in dry regions.


1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.


2009 ◽  
Vol 29 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Heiko Goelzer ◽  
Anders Levermann ◽  
Stefan Rahmstorf

2012 ◽  
Vol 43 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Manish Kumar Goyal ◽  
C. S. P. Ojha

We investigate the performance of existing state-of-the-art rule induction and tree algorithms, namely Single Conjunctive Rule Learner, Decision Table, M5 Model Tree, Decision Stump and REPTree. Downscaling models are developed using these algorithms to obtain projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these algorithms is evaluated through application to downscale the predictand for the Lake Pichola region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. M5 Model Tree algorithm was found to yield better performance among all other learning techniques explored in the present study. The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.


2015 ◽  
Vol 28 (20) ◽  
pp. 8093-8108 ◽  
Author(s):  
Cathryn E. Birch ◽  
Malcolm J. Roberts ◽  
Luis Garcia-Carreras ◽  
Duncan Ackerley ◽  
Michael J. Reeder ◽  
...  

Abstract There are some long-established biases in atmospheric models that originate from the representation of tropical convection. Previously, it has been difficult to separate cause and effect because errors are often the result of a number of interacting biases. Recently, researchers have gained the ability to run multiyear global climate model simulations with grid spacings small enough to switch the convective parameterization off, which permits the convection to develop explicitly. There are clear improvements to the initiation of convective storms and the diurnal cycle of rainfall in the convection-permitting simulations, which enables a new process-study approach to model bias identification. In this study, multiyear global atmosphere-only climate simulations with and without convective parameterization are undertaken with the Met Office Unified Model and are analyzed over the Maritime Continent region, where convergence from sea-breeze circulations is key for convection initiation. The analysis shows that, although the simulation with parameterized convection is able to reproduce the key rain-forming sea-breeze circulation, the parameterization is not able to respond realistically to the circulation. A feedback of errors also occurs: the convective parameterization causes rain to fall in the early morning, which cools and wets the boundary layer, reducing the land–sea temperature contrast and weakening the sea breeze. This is, however, an effect of the convective bias, rather than a cause of it. Improvements to how and when convection schemes trigger convection will improve both the timing and location of tropical rainfall and representation of sea-breeze circulations.


Sign in / Sign up

Export Citation Format

Share Document