# Presentation: 484107

## Land-atmosphere isotope exchange of water and CO2

- David Noone
*Department or Atmospheric and Oceanic Sciences*
* **Cooperative Institute for research in Environmental Sciences**University of Colorado at Boulder*

- Derek Brown, Nik Buenning (U. Colorado)Bill Riley (LBL), Chris Still (UCSB), Jim Randerson (UCI)

## Overview

- 18O isotopes in CO2 and water
- Atmospheric (troposperic) water vapor isotopes
- Simple models
***Basin scale estimate of recycling***
- Need for stronger constraintsMotivation for more complex (better?) models
- Motivation for integrated datasets (as we have just seen)
- ISOLSM, and ISOCLM – new development

## Motivation for isotopes

- Isotopes can help determine what processes are active.

- Complex interannual variability, might suggest multiple processes affect the measured signal

- CMDL Mauna Loa flask data

## IPCC projected precipitation

*IPCC, Fourth Assessment, Summary for Policy Makers, Feb 2007.*

- Percentage change 2080-2090 relative to 1980-1999.

- Patterns of changes linked to overturning circulation.
- But which set of hydrologic processes have changed?

## Reminder of isotope physics

**Two simple isotope models...**
***Condensation***
*Vapor becomes depleted as heavy removed preferentially*

- liquid (e.g., ocean)

- vapor(e.g., atmosphere)

***Evaporation***
*Returns to isotopic composition of the (ocean/land) source.*

**

- Ratio of HDO to H2O
- Measured as a difference from ocean water.

*Conditions under which condensation occurs is different from the conditions when evaporation occurs*

## Slide 6

## Worden, Bowman, Noone, et al. (2006)

- Fourier transform spectrometer
- Thermal infra-red (650 – 3050 cm-1)
- Individual lines resolved (0.06 cm-1)
- Primary mission O3, CO, CH4
- Micro-window contains H2O, CO2, HDO and H218O lines.
- Bayesian non-linear retrieval minimizes error in covariance HDO/H2O to precise isotope *ratio*

*Worden, Bowman, Noone, et al. (2006)*

- ~10 km hoz. resolution,
- ~200 km sampling,
- ~ 1 d.o.f. in vertical

## Annual mean D from TES

- Noone *et al.*, GEWEX, 2007

- Layer average 825 to 500 hPa (max. sensitivity)
- Data most reliable between 30N and 30S.

## Seasonal difference

- Wet season depletion - “amount effect”, evaporation of falling rain	(conditions dominated by condensation processes)
- Wet season enrichment? 	Different balance of fluxes.
***How important is land surface exchange/continental recycling in these regions?***

## The water cycle

*Rayleigh **condensation*

*Vapour in equilibrium with ocean water*

*Evaporation**(“mixing”)*

*Mean ~ -225 *permil
- Controlled by balance of condensation and boundary layer supply of oceanic water

- Fractionation efficiency ** ~ 1.1

- The water ***cycle***

- Requires rain to be evaporated to explain distribution of cloudy points, since this gives and “extra” fractionation
- Mass balance shows 30 % of rain evaporation needed
- Enriched values can not be from ocean – signal of evapotranspiration

## Amazon wet season

***Amazon wet season***
- Origin of air mass for most and lease depleted 25% of observations
- five-day back trajectories from all observations
- Most depleted spend more time over land (local convection and condensation)
- Least depleted more recently from the ocean.

## Processes modify distribution(e.g. Amazon)

- D (permil)

- Relative frequency

- Addition of D of source(close to D precipitation)

- Dashed D of 5-day origin 	(mostly tropical Atlantic)
- Solid D over Amazon

## Isotope mass balance (simple)

- Observations
*q, ****D*

**E**vapotranspiration

**A**dvection“transport”

*Ocean*

*Continent*

- Origin
*q, ****D*

- Rainfall evaporation and exchange

- Can be closed by assuming isotope physics (not unambiguous!)

- Ask, what fraction of atmospheric vapor comes from ET or ocean?

## Mass balance for “recycling”: E/C

- Condensation occurs as a Rayleigh process	 *R**condensation** = **** R **vapor*
- Evaporation takes usual Fickian form	*E = K(qsat – q), Ei = K(R**source**q**sat** – R**vapour** q)*
- Exchange and rain evaporation brings isotopic composition of raindrops toward that in equilibrium with boundary layer air	*R**precipitation** = g **** RPBL + (1-g) **** R**condensation*
*Mass balance:*

- Use *q* and *R* measurements to prescribe C then constrain *K* and *f*
- Assume g=0.7 (“cloud physics,” size of raindrops) and Rpbl (MIBA!).
- Rsource is soil water if transpiration (Land surface model, ISOLSM/ISOCLM)

## Local recycling or other

|  | Dry season |  |  |  | Wet season |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | E/C | f |  |  | E/C | f |  |
| Amazon | 97% | 0 | Continental recycling |  | 024% | > 6%30% | Advection source, strong conden. |
| N. Australia | 86% | 0 | Continental recycling |  | 0<20% | >3%30% | Advection source, strong conden. |
| SE Asia | 84% | 0 | Continental recycling |  | 69% | 0 | Continental recycling |

- D offers ONE constraint (either E/C or f)
- Based on TES D difference observation minus originE/C is ratio of source relative to sink over 5 daysf is the fraction of rainfall evaporation

## Limitations

- Poor knowledge of boundary layer isotopes
*MIBA, and better coordination of existing efforts (BASIN, GEWEX) will help*
- Same for soil water and precipitation
- Poor knowledge of cloud physics
*Better modeling, aircraft observations...?*
- Simplistic depiction of land surface
- ISOLSM/ISOCLM

## Isotopic model of water and carbon

**Water**Soil content (10 levels)Canopy interceptedSnow massLeaf water (isotopes)
**Carbon**Net soil biomassleaf, stem and root (isotopes)
**Drivers**
- Climate parameters
- H218O of vapour
- H218O of precipitation
- CO18O of air

- Coupled to climate model, or driven with observations

## Challenges in modeling

- Soil hydrology
- Exchange in plants
- Needs a new generation of isotope exchange
- (e.g.,  move beyond simple equilibrium/steady state based assumptions to explicit account of water transport in plants,
- Account for ‘back flux’ of water into soil/intercepted water/leaves
- ... Clearly these are the same issues. They are needed for isotopes, but isotopes can also act to constrain treatment.
- e.g., how much evaporation from intercepted water versus transpiration.
- Ultimately this is a very data limited problem
- Combining water measurements with CO2 offers additional constraints

## Conclusions

- Budget calculations of HDO and H2O can give P ***and*** E (rather than P-E)
- Hydrologic budgets as balance of ***fluxes*** directly from observations of isotope ***state***.
- Provide large (basin) scale estimate of “recycling”
- Requires knowledge of near surface isotopes
- Requires adequate models of isotope exchange in
- Isotopes suggest most precipitation comes from ET during wet seasonsaround 100% or more(!) In the Amazon
- Dry seasons more like 20-30% recycling.
- These can be tested against flux tower ET values.
- Isotope problem on the land surface integrates the CO2 with water via transpiration and soil hydrology